Overview

Brought to you by YData

Dataset statistics

Number of variables98
Number of observations38
Missing cells1284
Missing cells (%)34.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.2 KiB
Average record size in memory787.4 B

Variable types

Numeric7
Text35
Categorical44
Unsupported12

Alerts

ZL1 has constant value "TZL" Constant
MJ1 has constant value "t/rok" Constant
ZL2 has constant value "SO2" Constant
MJ2 has constant value "t/rok" Constant
ZL3 has constant value "NOx" Constant
MJ3 has constant value "t/rok" Constant
ZL4 has constant value "CO" Constant
MJ4 has constant value "t/rok" Constant
MJ7 has constant value "kg/rok" Constant
MJ8 has constant value "kg/rok" Constant
MJ9 has constant value "kg/rok" Constant
MJ10 has constant value "kg/rok" Constant
MJ11 has constant value "kg/rok" Constant
MJ12 has constant value "kg/rok" Constant
MJ13 has constant value "kg/rok" Constant
MJ14 has constant value "kg/rok" Constant
MJ15 has constant value "kg/rok" Constant
MJ16 has constant value "kg/rok" Constant
MJ17 has constant value "kg/rok" Constant
MJ18 has constant value "kg/rok" Constant
MJ19 has constant value "kg/rok" Constant
ZL22 has constant value "PCDD+PCDF" Constant
MJ22 has constant value "mg/rok" Constant
MNO22 has constant value "6.0" Constant
IC is highly overall correlated with MJ5 and 1 other fieldsHigh correlation
ICP is highly overall correlated with MJ21 and 1 other fieldsHigh correlation
KAPACITA (t/rok) is highly overall correlated with LINEK and 9 other fieldsHigh correlation
LINEK is highly overall correlated with KAPACITA (t/rok) and 1 other fieldsHigh correlation
MJ20 is highly overall correlated with KAPACITA (t/rok) and 19 other fieldsHigh correlation
MJ21 is highly overall correlated with ICP and 20 other fieldsHigh correlation
MJ5 is highly overall correlated with IC and 13 other fieldsHigh correlation
MJ6 is highly overall correlated with KAPACITA (t/rok) and 19 other fieldsHigh correlation
ODPAD is highly overall correlated with KAPACITA (t/rok) and 9 other fieldsHigh correlation
ROK is highly overall correlated with MJ21 and 1 other fieldsHigh correlation
S42_X is highly overall correlated with MJ21 and 2 other fieldsHigh correlation
S42_Y is highly overall correlated with S42_XHigh correlation
ZL10 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL11 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL12 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL13 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL14 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL15 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL16 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL17 is highly overall correlated with KAPACITA (t/rok) and 19 other fieldsHigh correlation
ZL18 is highly overall correlated with KAPACITA (t/rok) and 17 other fieldsHigh correlation
ZL19 is highly overall correlated with KAPACITA (t/rok) and 17 other fieldsHigh correlation
ZL20 is highly overall correlated with KAPACITA (t/rok) and 17 other fieldsHigh correlation
ZL21 is highly overall correlated with ICP and 20 other fieldsHigh correlation
ZL5 is highly overall correlated with IC and 13 other fieldsHigh correlation
ZL6 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL7 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL8 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL9 is highly overall correlated with MJ20 and 19 other fieldsHigh correlation
ZL5 is highly imbalanced (77.8%) Imbalance
MJ5 is highly imbalanced (77.8%) Imbalance
POZN has 13 (34.2%) missing values Missing
DAL has 8 (21.1%) missing values Missing
DRUHY has 9 (23.7%) missing values Missing
ZL1 has 10 (26.3%) missing values Missing
MJ1 has 10 (26.3%) missing values Missing
MNO1 has 10 (26.3%) missing values Missing
ZL2 has 10 (26.3%) missing values Missing
MJ2 has 10 (26.3%) missing values Missing
MNO2 has 10 (26.3%) missing values Missing
ZL3 has 10 (26.3%) missing values Missing
MJ3 has 10 (26.3%) missing values Missing
MNO3 has 10 (26.3%) missing values Missing
ZL4 has 10 (26.3%) missing values Missing
MJ4 has 10 (26.3%) missing values Missing
MNO4 has 10 (26.3%) missing values Missing
ZL5 has 10 (26.3%) missing values Missing
MJ5 has 10 (26.3%) missing values Missing
MNO5 has 10 (26.3%) missing values Missing
ZL6 has 10 (26.3%) missing values Missing
MJ6 has 10 (26.3%) missing values Missing
MNO6 has 10 (26.3%) missing values Missing
ZL7 has 10 (26.3%) missing values Missing
MJ7 has 10 (26.3%) missing values Missing
MNO7 has 10 (26.3%) missing values Missing
ZL8 has 10 (26.3%) missing values Missing
MJ8 has 10 (26.3%) missing values Missing
MNO8 has 10 (26.3%) missing values Missing
ZL9 has 10 (26.3%) missing values Missing
MJ9 has 10 (26.3%) missing values Missing
MNO9 has 10 (26.3%) missing values Missing
ZL10 has 10 (26.3%) missing values Missing
MJ10 has 10 (26.3%) missing values Missing
MNO10 has 10 (26.3%) missing values Missing
ZL11 has 10 (26.3%) missing values Missing
MJ11 has 10 (26.3%) missing values Missing
MNO11 has 10 (26.3%) missing values Missing
ZL12 has 10 (26.3%) missing values Missing
MJ12 has 10 (26.3%) missing values Missing
MNO12 has 10 (26.3%) missing values Missing
ZL13 has 10 (26.3%) missing values Missing
MJ13 has 10 (26.3%) missing values Missing
MNO13 has 10 (26.3%) missing values Missing
ZL14 has 10 (26.3%) missing values Missing
MJ14 has 10 (26.3%) missing values Missing
MNO14 has 10 (26.3%) missing values Missing
ZL15 has 10 (26.3%) missing values Missing
MJ15 has 10 (26.3%) missing values Missing
MNO15 has 10 (26.3%) missing values Missing
ZL16 has 10 (26.3%) missing values Missing
MJ16 has 10 (26.3%) missing values Missing
MNO16 has 10 (26.3%) missing values Missing
ZL17 has 10 (26.3%) missing values Missing
MJ17 has 10 (26.3%) missing values Missing
MNO17 has 10 (26.3%) missing values Missing
ZL18 has 10 (26.3%) missing values Missing
MJ18 has 10 (26.3%) missing values Missing
MNO18 has 10 (26.3%) missing values Missing
ZL19 has 10 (26.3%) missing values Missing
MJ19 has 10 (26.3%) missing values Missing
MNO19 has 10 (26.3%) missing values Missing
ZL20 has 10 (26.3%) missing values Missing
MJ20 has 10 (26.3%) missing values Missing
MNO20 has 10 (26.3%) missing values Missing
ZL21 has 29 (76.3%) missing values Missing
MJ21 has 29 (76.3%) missing values Missing
MNO21 has 29 (76.3%) missing values Missing
ZL22 has 37 (97.4%) missing values Missing
MJ22 has 37 (97.4%) missing values Missing
MNO22 has 37 (97.4%) missing values Missing
ZL23 has 38 (100.0%) missing values Missing
MJ23 has 38 (100.0%) missing values Missing
MNO23 has 38 (100.0%) missing values Missing
ZL24 has 38 (100.0%) missing values Missing
MJ24 has 38 (100.0%) missing values Missing
MNO24 has 38 (100.0%) missing values Missing
ZL25 has 38 (100.0%) missing values Missing
MJ25 has 38 (100.0%) missing values Missing
MNO25 has 38 (100.0%) missing values Missing
ZL26 has 38 (100.0%) missing values Missing
MJ26 has 38 (100.0%) missing values Missing
MNO26 has 38 (100.0%) missing values Missing
S42_X has unique values Unique
S42_Y has unique values Unique
ICP has unique values Unique
NAZEV has unique values Unique
NAZEV_Z has unique values Unique
ULICE_Z has unique values Unique
ZL23 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MJ23 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MNO23 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ZL24 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MJ24 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MNO24 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ZL25 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MJ25 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MNO25 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ZL26 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MJ26 is an unsupported type, check if it needs cleaning or further analysis Unsupported
MNO26 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ODPAD has 10 (26.3%) zeros Zeros

Reproduction

Analysis started2024-10-26 18:09:30.525871
Analysis finished2024-10-26 18:09:46.264199
Duration15.74 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

IC
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32853078
Minimum11835
Maximum65276124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:46.556678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11835
5-th percentile88326.85
Q125178430
median27590726
Q349681882
95-th percentile62749779
Maximum65276124
Range65264289
Interquartile range (IQR)24503452

Descriptive statistics

Standard deviation20582625
Coefficient of variation (CV)0.62650522
Kurtosis-0.96767585
Mean32853078
Median Absolute Deviation (MAD)12949458
Skewness0.11793601
Sum1.248417 × 109
Variance4.2364444 × 1014
MonotonicityNot monotonic
2024-10-26T20:09:46.890840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
25638955 4
 
10.5%
26209578 2
 
5.3%
61459364 2
 
5.3%
15526305 1
 
2.6%
65276124 1
 
2.6%
64203 1
 
2.6%
60194120 1
 
2.6%
27892638 1
 
2.6%
26227274 1
 
2.6%
27253236 1
 
2.6%
Other values (23) 23
60.5%
ValueCountFrequency (%)
11835 1
2.6%
64203 1
2.6%
92584 1
2.6%
179906 1
2.6%
183024 1
2.6%
14867494 1
2.6%
15504077 1
2.6%
15526305 1
2.6%
15531457 1
2.6%
25024922 1
2.6%
ValueCountFrequency (%)
65276124 1
2.6%
64650251 1
2.6%
62414402 1
2.6%
61459364 2
5.3%
60727772 1
2.6%
60713470 1
2.6%
60709286 1
2.6%
60194120 1
2.6%
49790480 1
2.6%
49356089 1
2.6%

S42_X
Real number (ℝ)

High correlation  Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3542705.7
Minimum3378991
Maximum3731925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:47.057518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3378991
5-th percentile3409599.5
Q13456584.5
median3545425.5
Q33620514.8
95-th percentile3702181.9
Maximum3731925
Range352934
Interquartile range (IQR)163930.25

Descriptive statistics

Standard deviation98531.007
Coefficient of variation (CV)0.02781236
Kurtosis-0.94777377
Mean3542705.7
Median Absolute Deviation (MAD)81255.5
Skewness0.23835565
Sum1.3462282 × 108
Variance9.7083594 × 109
MonotonicityNot monotonic
2024-10-26T20:09:47.264197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3476995 1
 
2.6%
3452534 1
 
2.6%
3478520 1
 
2.6%
3385434 1
 
2.6%
3453202 1
 
2.6%
3466732 1
 
2.6%
3546126 1
 
2.6%
3653215 1
 
2.6%
3646688 1
 
2.6%
3549923 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
3378991 1
2.6%
3385434 1
2.6%
3413864 1
2.6%
3420802 1
2.6%
3428982 1
2.6%
3431271 1
2.6%
3431731 1
2.6%
3452432 1
2.6%
3452534 1
2.6%
3453202 1
2.6%
ValueCountFrequency (%)
3731925 1
2.6%
3715152 1
2.6%
3699893 1
2.6%
3690095 1
2.6%
3679332 1
2.6%
3672463 1
2.6%
3653215 1
2.6%
3646688 1
2.6%
3629243 1
2.6%
3621505 1
2.6%

S42_Y
Real number (ℝ)

High correlation  Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5521502.5
Minimum5415800
Maximum5625042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:47.447548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5415800
5-th percentile5450591
Q15472911.2
median5523392
Q35551636.2
95-th percentile5616258.5
Maximum5625042
Range209242
Interquartile range (IQR)78725

Descriptive statistics

Standard deviation55999.753
Coefficient of variation (CV)0.010142122
Kurtosis-0.81114347
Mean5521502.5
Median Absolute Deviation (MAD)43859.5
Skewness0.225547
Sum2.0981709 × 108
Variance3.1359723 × 109
MonotonicityNot monotonic
2024-10-26T20:09:47.601766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5517233 1
 
2.6%
5540821 1
 
2.6%
5470822 1
 
2.6%
5511902 1
 
2.6%
5549068 1
 
2.6%
5552394 1
 
2.6%
5529796 1
 
2.6%
5484366 1
 
2.6%
5465379 1
 
2.6%
5546405 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
5415800 1
2.6%
5439632 1
2.6%
5452525 1
2.6%
5455260 1
2.6%
5456356 1
2.6%
5459097 1
2.6%
5464870 1
2.6%
5465379 1
2.6%
5470822 1
2.6%
5471818 1
2.6%
ValueCountFrequency (%)
5625042 1
2.6%
5623826 1
2.6%
5614923 1
2.6%
5612951 1
2.6%
5605943 1
2.6%
5595892 1
2.6%
5569633 1
2.6%
5564870 1
2.6%
5563631 1
2.6%
5552394 1
2.6%

ICP
Real number (ℝ)

High correlation  Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9867273 × 108
Minimum6.0219008 × 108
Maximum7.9341011 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:47.764441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.0219008 × 108
5-th percentile6.1009489 × 108
Q16.5351014 × 108
median7.1012408 × 108
Q37.3819261 × 108
95-th percentile7.7518933 × 108
Maximum7.9341011 × 108
Range1.9122003 × 108
Interquartile range (IQR)84682471

Descriptive statistics

Standard deviation55379783
Coefficient of variation (CV)0.07926427
Kurtosis-1.1425385
Mean6.9867273 × 108
Median Absolute Deviation (MAD)48024705
Skewness-0.14278159
Sum2.6549564 × 1010
Variance3.0669204 × 1015
MonotonicityStrictly increasing
2024-10-26T20:09:47.965651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
602190081 1
 
2.6%
738620091 1
 
2.6%
721330133 1
 
2.6%
722248051 1
 
2.6%
728951001 1
 
2.6%
732450771 1
 
2.6%
732800013 1
 
2.6%
733490311 1
 
2.6%
736910171 1
 
2.6%
743850321 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
602190081 1
2.6%
604340041 1
2.6%
611110451 1
2.6%
612958121 1
2.6%
624100031 1
2.6%
635988081 1
2.6%
646870171 1
2.6%
647680111 1
2.6%
648696033 1
2.6%
653270043 1
2.6%
ValueCountFrequency (%)
793410111 1
2.6%
776430491 1
2.6%
774970301 1
2.6%
774878221 1
2.6%
772840161 1
2.6%
765490013 1
2.6%
755910031 1
2.6%
747840061 1
2.6%
743850321 1
2.6%
738620091 1
2.6%

NAZEV
Text

Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:48.389675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length113
Median length47.5
Mean length46.631579
Min length13

Characters and Unicode

Total characters1772
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowNemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského kraje – Kotelna a spalovna
2nd rowÈEZ, a.s. ? Elektrárna Ledvice
3rd rowSAKO Brno, a.s. – divize 3 ZEVO
4th rowEnvir s.r.o. – Spalovna NO Brtnice
5th rowLafarge Cement, a.s.
ValueCountFrequency (%)
– 28
 
10.2%
spalovna 21
 
7.7%
a.s 19
 
6.9%
s.r.o 11
 
4.0%
nemocnice 8
 
2.9%
a 7
 
2.6%
odpadù 7
 
2.6%
no 6
 
2.2%
využití 5
 
1.8%
kotelna 5
 
1.8%
Other values (121) 157
57.3%
2024-10-26T20:09:48.960067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
236
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.2%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
236
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.2%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
236
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.2%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
236
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.2%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.1%

POZN
Text

Missing 

Distinct20
Distinct (%)80.0%
Missing13
Missing (%)34.2%
Memory size432.0 B
2024-10-26T20:09:49.305653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length403
Median length238
Mean length172.16
Min length29

Characters and Unicode

Total characters4304
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)72.0%

Sample

1st rowZmìnou integrovaného povolení è. 27 ze dne 31. 10. 2022 byla povolena spalovací zkouška kalù z likvidace odpadních vod spolu s hnìdým uhlím. Emise pocházejí pøevážnì ze spalování uhlí.
2nd rowNa základì písemného oznámení provozovatele je od 2. 3. 2015 pozastaveno spalování odpadu z dùvodu odpojení spalovny od dodávek elektrické energie, do konce roku 2022 nebylo zahájeno. Bylo zahájeno posuzování vlivù na životní prostøedí k zámìru „Modernizace spalovny NO Brtnice s cílem navýšení zpracovatelské kapacity“. Plánovaná kapacita je 2 800 t/rok.
3rd rowSpoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèní peci, pouze malá èást ze spoluspalování odpadu.
4th rowOd 19 .4. 2022 došlo ke zmìnì názvu provozovatele (døíve SUEZ CZ a.s.).
5th rowSpoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèní peci, pouze malá èást ze spoluspalování odpadu.
ValueCountFrequency (%)
2022 17
 
2.6%
z 16
 
2.4%
v 16
 
2.4%
od 15
 
2.3%
byla 13
 
2.0%
dne 12
 
1.8%
spoluspalování 10
 
1.5%
ze 9
 
1.4%
do 9
 
1.4%
roku 8
 
1.2%
Other values (288) 536
81.1%
2024-10-26T20:09:49.906968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
638
 
14.8%
o 391
 
9.1%
n 276
 
6.4%
e 244
 
5.7%
a 218
 
5.1%
v 204
 
4.7%
p 174
 
4.0%
l 142
 
3.3%
d 141
 
3.3%
z 128
 
3.0%
Other values (62) 1748
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
638
 
14.8%
o 391
 
9.1%
n 276
 
6.4%
e 244
 
5.7%
a 218
 
5.1%
v 204
 
4.7%
p 174
 
4.0%
l 142
 
3.3%
d 141
 
3.3%
z 128
 
3.0%
Other values (62) 1748
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
638
 
14.8%
o 391
 
9.1%
n 276
 
6.4%
e 244
 
5.7%
a 218
 
5.1%
v 204
 
4.7%
p 174
 
4.0%
l 142
 
3.3%
d 141
 
3.3%
z 128
 
3.0%
Other values (62) 1748
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
638
 
14.8%
o 391
 
9.1%
n 276
 
6.4%
e 244
 
5.7%
a 218
 
5.1%
v 204
 
4.7%
p 174
 
4.0%
l 142
 
3.3%
d 141
 
3.3%
z 128
 
3.0%
Other values (62) 1748
40.6%

NAZEV_P
Text

Distinct33
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:50.178592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length73
Median length30
Mean length24.789474
Min length9

Characters and Unicode

Total characters942
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)78.9%

Sample

1st rowNemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského kraje
2nd rowÈEZ, a.s.
3rd rowSAKO Brno, a.s.
4th rowEnvir s.r.o.
5th rowLafarge Cement, a.s.
ValueCountFrequency (%)
a.s 19
 
13.3%
s.r.o 11
 
7.7%
nemocnice 7
 
4.9%
recovera 4
 
2.8%
zdrojù 4
 
2.8%
využití 4
 
2.8%
cement 4
 
2.8%
a 3
 
2.1%
s 3
 
2.1%
e 3
 
2.1%
Other values (72) 81
56.6%
2024-10-26T20:09:50.634147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
 
10.8%
. 79
 
8.4%
o 64
 
6.8%
e 57
 
6.1%
s 55
 
5.8%
a 52
 
5.5%
r 44
 
4.7%
n 32
 
3.4%
c 28
 
3.0%
i 23
 
2.4%
Other values (60) 406
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
102
 
10.8%
. 79
 
8.4%
o 64
 
6.8%
e 57
 
6.1%
s 55
 
5.8%
a 52
 
5.5%
r 44
 
4.7%
n 32
 
3.4%
c 28
 
3.0%
i 23
 
2.4%
Other values (60) 406
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
102
 
10.8%
. 79
 
8.4%
o 64
 
6.8%
e 57
 
6.1%
s 55
 
5.8%
a 52
 
5.5%
r 44
 
4.7%
n 32
 
3.4%
c 28
 
3.0%
i 23
 
2.4%
Other values (60) 406
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
102
 
10.8%
. 79
 
8.4%
o 64
 
6.8%
e 57
 
6.1%
s 55
 
5.8%
a 52
 
5.5%
r 44
 
4.7%
n 32
 
3.4%
c 28
 
3.0%
i 23
 
2.4%
Other values (60) 406
43.1%

ULICE_P
Text

Distinct33
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:50.969923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length14.184211
Min length9

Characters and Unicode

Total characters539
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)78.9%

Sample

1st rowMáchova 400
2nd rowDuhová 1444
3rd rowJedovnická 4247
4th rowBuštìhradská 998
5th rowÈížkovice 27
ValueCountFrequency (%)
Španìlská 4
 
4.5%
1073 4
 
4.5%
1746 2
 
2.3%
u 2
 
2.3%
revoluèní 2
 
2.3%
klimentská 2
 
2.3%
359 2
 
2.3%
mokrá 2
 
2.3%
810 1
 
1.1%
komenského 1
 
1.1%
Other values (66) 66
75.0%
2024-10-26T20:09:51.537160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
9.3%
o 27
 
5.0%
k 26
 
4.8%
a 23
 
4.3%
á 21
 
3.9%
n 20
 
3.7%
1 20
 
3.7%
s 18
 
3.3%
4 18
 
3.3%
e 17
 
3.2%
Other values (52) 299
55.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
50
 
9.3%
o 27
 
5.0%
k 26
 
4.8%
a 23
 
4.3%
á 21
 
3.9%
n 20
 
3.7%
1 20
 
3.7%
s 18
 
3.3%
4 18
 
3.3%
e 17
 
3.2%
Other values (52) 299
55.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
50
 
9.3%
o 27
 
5.0%
k 26
 
4.8%
a 23
 
4.3%
á 21
 
3.9%
n 20
 
3.7%
1 20
 
3.7%
s 18
 
3.3%
4 18
 
3.3%
e 17
 
3.2%
Other values (52) 299
55.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
50
 
9.3%
o 27
 
5.0%
k 26
 
4.8%
a 23
 
4.3%
á 21
 
3.9%
n 20
 
3.7%
1 20
 
3.7%
s 18
 
3.3%
4 18
 
3.3%
e 17
 
3.2%
Other values (52) 299
55.5%

OBEC_P
Text

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:51.825818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length24.5
Mean length15.447368
Min length10

Characters and Unicode

Total characters587
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)73.7%

Sample

1st row25601 Benešov
2nd row14000 Praha 4
3rd row62800 Brno
4th row27201 Kladno
5th row41112 Èížkovice
ValueCountFrequency (%)
praha 13
 
12.4%
12000 4
 
3.8%
2 4
 
3.8%
11000 4
 
3.8%
1 4
 
3.8%
nad 3
 
2.9%
66404 2
 
1.9%
mokrá-horákov 2
 
1.9%
králové 2
 
1.9%
hradec 2
 
1.9%
Other values (63) 65
61.9%
2024-10-26T20:09:52.395770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 71
 
12.1%
67
 
11.4%
a 45
 
7.7%
1 39
 
6.6%
r 32
 
5.5%
o 24
 
4.1%
6 22
 
3.7%
2 19
 
3.2%
5 17
 
2.9%
P 17
 
2.9%
Other values (46) 234
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71
 
12.1%
67
 
11.4%
a 45
 
7.7%
1 39
 
6.6%
r 32
 
5.5%
o 24
 
4.1%
6 22
 
3.7%
2 19
 
3.2%
5 17
 
2.9%
P 17
 
2.9%
Other values (46) 234
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71
 
12.1%
67
 
11.4%
a 45
 
7.7%
1 39
 
6.6%
r 32
 
5.5%
o 24
 
4.1%
6 22
 
3.7%
2 19
 
3.2%
5 17
 
2.9%
P 17
 
2.9%
Other values (46) 234
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71
 
12.1%
67
 
11.4%
a 45
 
7.7%
1 39
 
6.6%
r 32
 
5.5%
o 24
 
4.1%
6 22
 
3.7%
2 19
 
3.2%
5 17
 
2.9%
P 17
 
2.9%
Other values (46) 234
39.9%

NAZEV_Z
Text

Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:52.773461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length113
Median length49.5
Mean length46.605263
Min length13

Characters and Unicode

Total characters1771
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowNemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského kraje – Kotelna a spalovna
2nd rowÈEZ, a.s. ? Elektrárna Ledvice
3rd rowSAKO Brno, a.s. – divize 3 ZEVO
4th rowEnvir s.r.o. – Spalovna NO Brtnice
5th rowLafarge Cement, a.s.
ValueCountFrequency (%)
– 28
 
10.2%
spalovna 21
 
7.7%
a.s 19
 
6.9%
s.r.o 11
 
4.0%
nemocnice 8
 
2.9%
a 7
 
2.6%
odpadù 7
 
2.6%
no 6
 
2.2%
využití 5
 
1.8%
kotelna 5
 
1.8%
Other values (121) 157
57.3%
2024-10-26T20:09:53.307523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.3%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
235
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.3%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
235
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.3%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
235
 
13.3%
a 133
 
7.5%
o 124
 
7.0%
e 93
 
5.3%
n 84
 
4.7%
. 80
 
4.5%
s 66
 
3.7%
r 62
 
3.5%
l 57
 
3.2%
v 55
 
3.1%
Other values (67) 782
44.2%

ULICE_Z
Text

Unique 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:53.684427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length55
Median length23
Mean length15.552632
Min length1

Characters and Unicode

Total characters591
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowMáchova 400
2nd rowOsada 141
3rd rowJedovnická 4247
4th rowPod Kaplí 179
5th rowÈížkovice 27
ValueCountFrequency (%)
lokalita 2
 
2.0%
prùmyslová 2
 
2.0%
u 2
 
2.0%
máchova 1
 
1.0%
1180 1
 
1.0%
skládky 1
 
1.0%
69 1
 
1.0%
dvory 1
 
1.0%
hruškové 1
 
1.0%
288 1
 
1.0%
Other values (85) 85
86.7%
2024-10-26T20:09:54.208599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
10.8%
o 36
 
6.1%
k 31
 
5.2%
a 25
 
4.2%
l 23
 
3.9%
e 21
 
3.6%
á 21
 
3.6%
r 21
 
3.6%
1 21
 
3.6%
v 21
 
3.6%
Other values (53) 307
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
64
 
10.8%
o 36
 
6.1%
k 31
 
5.2%
a 25
 
4.2%
l 23
 
3.9%
e 21
 
3.6%
á 21
 
3.6%
r 21
 
3.6%
1 21
 
3.6%
v 21
 
3.6%
Other values (53) 307
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
64
 
10.8%
o 36
 
6.1%
k 31
 
5.2%
a 25
 
4.2%
l 23
 
3.9%
e 21
 
3.6%
á 21
 
3.6%
r 21
 
3.6%
1 21
 
3.6%
v 21
 
3.6%
Other values (53) 307
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
64
 
10.8%
o 36
 
6.1%
k 31
 
5.2%
a 25
 
4.2%
l 23
 
3.9%
e 21
 
3.6%
á 21
 
3.6%
r 21
 
3.6%
1 21
 
3.6%
v 21
 
3.6%
Other values (53) 307
51.9%

OBEC_Z
Text

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:54.545947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length15.526316
Min length10

Characters and Unicode

Total characters590
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)94.7%

Sample

1st row25601 Benešov
2nd row41801 Bílina
3rd row62800 Brno
4th row58832 Brtnice
5th row41112 Èížkovice
ValueCountFrequency (%)
nad 5
 
5.2%
praha 3
 
3.1%
58601 2
 
2.1%
labem 2
 
2.1%
jihlava 2
 
2.1%
chotíkov 1
 
1.0%
76302 1
 
1.0%
76811 1
 
1.0%
hranice 1
 
1.0%
75301 1
 
1.0%
Other values (77) 77
80.2%
2024-10-26T20:09:55.239365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
9.8%
0 45
 
7.6%
a 34
 
5.8%
1 25
 
4.2%
6 24
 
4.1%
o 24
 
4.1%
r 22
 
3.7%
n 21
 
3.6%
5 20
 
3.4%
e 19
 
3.2%
Other values (50) 298
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
58
 
9.8%
0 45
 
7.6%
a 34
 
5.8%
1 25
 
4.2%
6 24
 
4.1%
o 24
 
4.1%
r 22
 
3.7%
n 21
 
3.6%
5 20
 
3.4%
e 19
 
3.2%
Other values (50) 298
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
58
 
9.8%
0 45
 
7.6%
a 34
 
5.8%
1 25
 
4.2%
6 24
 
4.1%
o 24
 
4.1%
r 22
 
3.7%
n 21
 
3.6%
5 20
 
3.4%
e 19
 
3.2%
Other values (50) 298
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
58
 
9.8%
0 45
 
7.6%
a 34
 
5.8%
1 25
 
4.2%
6 24
 
4.1%
o 24
 
4.1%
r 22
 
3.7%
n 21
 
3.6%
5 20
 
3.4%
e 19
 
3.2%
Other values (50) 298
50.5%

ROK
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.2368
Minimum1961
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:55.436732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1961
5-th percentile1973.95
Q11993
median1996
Q32001.75
95-th percentile2017.45
Maximum2022
Range61
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation12.739456
Coefficient of variation (CV)0.0063817358
Kurtosis1.308206
Mean1996.2368
Median Absolute Deviation (MAD)4
Skewness-0.52643579
Sum75857
Variance162.29374
MonotonicityNot monotonic
2024-10-26T20:09:55.585672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1993 6
 
15.8%
2000 4
 
10.5%
1994 3
 
7.9%
2005 2
 
5.3%
1996 2
 
5.3%
2007 2
 
5.3%
1997 1
 
2.6%
1990 1
 
2.6%
1968 1
 
2.6%
1961 1
 
2.6%
Other values (15) 15
39.5%
ValueCountFrequency (%)
1961 1
 
2.6%
1968 1
 
2.6%
1975 1
 
2.6%
1976 1
 
2.6%
1979 1
 
2.6%
1989 1
 
2.6%
1990 1
 
2.6%
1992 1
 
2.6%
1993 6
15.8%
1994 3
7.9%
ValueCountFrequency (%)
2022 1
 
2.6%
2020 1
 
2.6%
2017 1
 
2.6%
2016 1
 
2.6%
2007 2
5.3%
2005 2
5.3%
2004 1
 
2.6%
2002 1
 
2.6%
2001 1
 
2.6%
2000 4
10.5%

DAL
Text

Missing 

Distinct29
Distinct (%)96.7%
Missing8
Missing (%)21.1%
Memory size432.0 B
2024-10-26T20:09:56.068582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length216
Median length90
Mean length84.866667
Min length11

Characters and Unicode

Total characters2546
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row2004 – instalace technologie na záchyt PCDD/F, 2009 – rekonstrukce systému na èištìní spalin
2nd row1994 – spuštìní 2. stupnì èištìní spalin, 2004 – SNCR, 2009–2010 – rekonstrukce zaøízení
3rd row2006 – renovace filtru GORE-TEX, 2011 – instalace kontinuálního mìøení emisí
4th row2006 – SNCR
5th row2017 – náhrada dvou elektroodluèovaèù za rotaèní pecí jedním textilním hadicovým filtrem
ValueCountFrequency (%)
– 48
 
12.9%
2004 11
 
3.0%
rekonstrukce 9
 
2.4%
èištìní 8
 
2.2%
spalin 8
 
2.2%
zaøízení 7
 
1.9%
instalace 7
 
1.9%
technologie 7
 
1.9%
filtr 6
 
1.6%
na 6
 
1.6%
Other values (167) 254
68.5%
2024-10-26T20:09:56.715532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
344
 
13.5%
n 158
 
6.2%
o 128
 
5.0%
a 119
 
4.7%
t 118
 
4.6%
e 117
 
4.6%
i 101
 
4.0%
0 85
 
3.3%
r 81
 
3.2%
s 72
 
2.8%
Other values (63) 1223
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
344
 
13.5%
n 158
 
6.2%
o 128
 
5.0%
a 119
 
4.7%
t 118
 
4.6%
e 117
 
4.6%
i 101
 
4.0%
0 85
 
3.3%
r 81
 
3.2%
s 72
 
2.8%
Other values (63) 1223
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
344
 
13.5%
n 158
 
6.2%
o 128
 
5.0%
a 119
 
4.7%
t 118
 
4.6%
e 117
 
4.6%
i 101
 
4.0%
0 85
 
3.3%
r 81
 
3.2%
s 72
 
2.8%
Other values (63) 1223
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
344
 
13.5%
n 158
 
6.2%
o 128
 
5.0%
a 119
 
4.7%
t 118
 
4.6%
e 117
 
4.6%
i 101
 
4.0%
0 85
 
3.3%
r 81
 
3.2%
s 72
 
2.8%
Other values (63) 1223
48.0%

DRUHY
Text

Missing 

Distinct21
Distinct (%)72.4%
Missing9
Missing (%)23.7%
Memory size432.0 B
2024-10-26T20:09:57.105598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length287
Median length143
Mean length93.068966
Min length22

Characters and Unicode

Total characters2699
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)58.6%

Sample

1st rownemocnièní a zdravotnické odpady
2nd rowkaly z likvidace odpadních vod ( 19 08 14 ? kaly z jiných zpùsobù èištìní prùmyslových odpadních vod neuvedené pod èíslem 19 08 13)
3rd rowsmìsný komunální odpad
4th rowhnìdouhelný generátorový dehet, stabilizované kaly (sludge), spalitelný odpad (palivo vyrobené z odpadu), celé i drcené pneumatiky, surový odpadní benzin, odpadní oleje, øedidla a glycerin, Lipix, døevní odpad, drcená odpadní pryž, vlastní odpady, briketovaný odpad (skelná vlákna/plast)
5th rowodpady z rùzných odvìtví prùm. èinnosti, odpady ze zdravotní a veterinární péèe
ValueCountFrequency (%)
odpady 34
 
9.4%
z 22
 
6.1%
odpadní 19
 
5.2%
a 18
 
5.0%
odpad 13
 
3.6%
obaly 9
 
2.5%
odvìtví 8
 
2.2%
oleje 8
 
2.2%
prùm 8
 
2.2%
èinnosti 8
 
2.2%
Other values (111) 215
59.4%
2024-10-26T20:09:57.664187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
333
 
12.3%
d 203
 
7.5%
o 202
 
7.5%
a 191
 
7.1%
n 184
 
6.8%
p 132
 
4.9%
e 114
 
4.2%
i 98
 
3.6%
t 96
 
3.6%
r 92
 
3.4%
Other values (47) 1054
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
333
 
12.3%
d 203
 
7.5%
o 202
 
7.5%
a 191
 
7.1%
n 184
 
6.8%
p 132
 
4.9%
e 114
 
4.2%
i 98
 
3.6%
t 96
 
3.6%
r 92
 
3.4%
Other values (47) 1054
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
333
 
12.3%
d 203
 
7.5%
o 202
 
7.5%
a 191
 
7.1%
n 184
 
6.8%
p 132
 
4.9%
e 114
 
4.2%
i 98
 
3.6%
t 96
 
3.6%
r 92
 
3.4%
Other values (47) 1054
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
333
 
12.3%
d 203
 
7.5%
o 202
 
7.5%
a 191
 
7.1%
n 184
 
6.8%
p 132
 
4.9%
e 114
 
4.2%
i 98
 
3.6%
t 96
 
3.6%
r 92
 
3.4%
Other values (47) 1054
39.1%

KAPACITA (t/rok)
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39864.605
Minimum70
Maximum330000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:57.849522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile311
Q11825
median3750
Q323250
95-th percentile178674
Maximum330000
Range329930
Interquartile range (IQR)21425

Descriptive statistics

Standard deviation74816.383
Coefficient of variation (CV)1.8767622
Kurtosis6.301994
Mean39864.605
Median Absolute Deviation (MAD)3175
Skewness2.4614754
Sum1514855
Variance5.5974912 × 109
MonotonicityNot monotonic
2024-10-26T20:09:58.004357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
750 2
 
5.3%
1900 2
 
5.3%
3500 2
 
5.3%
1000 1
 
2.6%
15800 1
 
2.6%
330000 1
 
2.6%
166440 1
 
2.6%
4000 1
 
2.6%
3240 1
 
2.6%
88000 1
 
2.6%
Other values (25) 25
65.8%
ValueCountFrequency (%)
70 1
2.6%
90 1
2.6%
350 1
2.6%
400 1
2.6%
750 2
5.3%
780 1
2.6%
864 1
2.6%
1000 1
2.6%
1800 1
2.6%
1900 2
5.3%
ValueCountFrequency (%)
330000 1
2.6%
248000 1
2.6%
166440 1
2.6%
130000 1
2.6%
120000 1
2.6%
113800 1
2.6%
96000 1
2.6%
88000 1
2.6%
80000 1
2.6%
25000 1
2.6%

ODPAD
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30215.132
Minimum0
Maximum270836
Zeros10
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size432.0 B
2024-10-26T20:09:58.184367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median1685.5
Q314794.25
95-th percentile131707.3
Maximum270836
Range270836
Interquartile range (IQR)14794

Descriptive statistics

Standard deviation63553.68
Coefficient of variation (CV)2.1033726
Kurtosis7.5014731
Mean30215.132
Median Absolute Deviation (MAD)1685.5
Skewness2.7206964
Sum1148175
Variance4.0390703 × 109
MonotonicityNot monotonic
2024-10-26T20:09:58.334948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 10
26.3%
806 1
 
2.6%
1740 1
 
2.6%
6874 1
 
2.6%
9725 1
 
2.6%
2630 1
 
2.6%
349 1
 
2.6%
1 1
 
2.6%
1631 1
 
2.6%
80090 1
 
2.6%
Other values (19) 19
50.0%
ValueCountFrequency (%)
0 10
26.3%
1 1
 
2.6%
15 1
 
2.6%
349 1
 
2.6%
653 1
 
2.6%
806 1
 
2.6%
847 1
 
2.6%
1056 1
 
2.6%
1521 1
 
2.6%
1631 1
 
2.6%
ValueCountFrequency (%)
270836 1
2.6%
242532 1
2.6%
112150 1
2.6%
111698 1
2.6%
89896 1
2.6%
86800 1
2.6%
80090 1
2.6%
60669 1
2.6%
23791 1
2.6%
14934 1
2.6%

LINEK
Categorical

High correlation 

Distinct4
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size432.0 B
1
30 
2
5 
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

Length

2024-10-26T20:09:58.533671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:09:58.719711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 30
78.9%
2 5
 
13.2%
3 2
 
5.3%
4 1
 
2.6%

LINKY
Text

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:09:59.098838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length108
Median length61
Mean length52.210526
Min length13

Characters and Unicode

Total characters1984
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)81.6%

Sample

1st rowspalovna PL-10-200
2nd rowblok B4 s fluidním kotlem
3rd rowspalovenské kotle K2 a K3 (systém Düsseldorf)
4th rowdvoustupòová spalovací pec HOVAL GG 17 (pyrolýzní komora a dopalovací termoreaktor)
5th rowrotaèní pec na výrobu slínku
ValueCountFrequency (%)
pec 18
 
6.2%
spalovací 13
 
4.5%
rotaèní 12
 
4.1%
a 12
 
4.1%
pyrolýzní 12
 
4.1%
dopalovací 10
 
3.4%
gg 9
 
3.1%
termoreaktor 9
 
3.1%
komora 9
 
3.1%
hoval 9
 
3.1%
Other values (115) 179
61.3%
2024-10-26T20:09:59.655320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
254
 
12.8%
o 196
 
9.9%
a 141
 
7.1%
p 95
 
4.8%
r 91
 
4.6%
v 77
 
3.9%
e 77
 
3.9%
l 74
 
3.7%
s 74
 
3.7%
í 73
 
3.7%
Other values (63) 832
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
254
 
12.8%
o 196
 
9.9%
a 141
 
7.1%
p 95
 
4.8%
r 91
 
4.6%
v 77
 
3.9%
e 77
 
3.9%
l 74
 
3.7%
s 74
 
3.7%
í 73
 
3.7%
Other values (63) 832
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
254
 
12.8%
o 196
 
9.9%
a 141
 
7.1%
p 95
 
4.8%
r 91
 
4.6%
v 77
 
3.9%
e 77
 
3.9%
l 74
 
3.7%
s 74
 
3.7%
í 73
 
3.7%
Other values (63) 832
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
254
 
12.8%
o 196
 
9.9%
a 141
 
7.1%
p 95
 
4.8%
r 91
 
4.6%
v 77
 
3.9%
e 77
 
3.9%
l 74
 
3.7%
s 74
 
3.7%
í 73
 
3.7%
Other values (63) 832
41.9%

PLYNY
Text

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-10-26T20:10:00.005672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length249
Median length118.5
Mean length99.657895
Min length26

Characters and Unicode

Total characters3787
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)89.5%

Sample

1st rowsuchá vypírka spalin (adsorpce), tkaninový rukávcový filtr, dioxinový filtr s aktivním koksem, alkalická vypírka spalin
2nd rowsuché odsíøení pomocí vápence ve fluidním loži, elektrostatický odluèovaè
3rd rownekatalytická redukce oxidù dusíku nástøikem roztoku moèoviny, aktivní uhlí, absorpce plynù vápennou vypírkou, textilní filtr s vláknitou vrstvou
4th rowsuchý filtr GORE-TEX, praèka kouøových plynù DRY-WET ÖSKO
5th rowelektrostatický tøíkomorový odluèovaè
ValueCountFrequency (%)
filtr 25
 
5.0%
s 22
 
4.4%
a 18
 
3.6%
vypírka 14
 
2.8%
spalin 11
 
2.2%
uhlí 11
 
2.2%
pomocí 9
 
1.8%
textilní 9
 
1.8%
aktivního 8
 
1.6%
dioxinový 8
 
1.6%
Other values (189) 361
72.8%
2024-10-26T20:10:00.545397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
459
 
12.1%
o 247
 
6.5%
t 200
 
5.3%
a 197
 
5.2%
k 180
 
4.8%
i 172
 
4.5%
v 169
 
4.5%
n 160
 
4.2%
l 158
 
4.2%
r 142
 
3.7%
Other values (66) 1703
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
459
 
12.1%
o 247
 
6.5%
t 200
 
5.3%
a 197
 
5.2%
k 180
 
4.8%
i 172
 
4.5%
v 169
 
4.5%
n 160
 
4.2%
l 158
 
4.2%
r 142
 
3.7%
Other values (66) 1703
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
459
 
12.1%
o 247
 
6.5%
t 200
 
5.3%
a 197
 
5.2%
k 180
 
4.8%
i 172
 
4.5%
v 169
 
4.5%
n 160
 
4.2%
l 158
 
4.2%
r 142
 
3.7%
Other values (66) 1703
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
459
 
12.1%
o 247
 
6.5%
t 200
 
5.3%
a 197
 
5.2%
k 180
 
4.8%
i 172
 
4.5%
v 169
 
4.5%
n 160
 
4.2%
l 158
 
4.2%
r 142
 
3.7%
Other values (66) 1703
45.0%

ZL1
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
TZL
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTZL
2nd rowTZL
3rd rowTZL
4th rowTZL
5th rowTZL

Common Values

ValueCountFrequency (%)
TZL 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:00.739948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:00.865525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tzl 28
100.0%

Most occurring characters

ValueCountFrequency (%)
T 28
33.3%
Z 28
33.3%
L 28
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 28
33.3%
Z 28
33.3%
L 28
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 28
33.3%
Z 28
33.3%
L 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 28
33.3%
Z 28
33.3%
L 28
33.3%

MJ1
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
t/rok
28 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt/rok
2nd rowt/rok
3rd rowt/rok
4th rowt/rok
5th rowt/rok

Common Values

ValueCountFrequency (%)
t/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:00.995298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:01.110874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

MNO1
Text

Missing 

Distinct25
Distinct (%)89.3%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:01.297992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6785714
Min length1

Characters and Unicode

Total characters131
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)78.6%

Sample

1st row0,059
2nd row23,258
3rd row0,326
4th row3,476
5th row0,005
ValueCountFrequency (%)
0,059 2
 
7.1%
0 2
 
7.1%
0,005 2
 
7.1%
0,049 1
 
3.6%
0,326 1
 
3.6%
3,476 1
 
3.6%
4,014 1
 
3.6%
0,065 1
 
3.6%
0,091 1
 
3.6%
0,073 1
 
3.6%
Other values (15) 15
53.6%
2024-10-26T20:10:01.664014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43
32.8%
, 26
19.8%
5 10
 
7.6%
1 9
 
6.9%
2 9
 
6.9%
4 8
 
6.1%
6 7
 
5.3%
3 7
 
5.3%
9 5
 
3.8%
7 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43
32.8%
, 26
19.8%
5 10
 
7.6%
1 9
 
6.9%
2 9
 
6.9%
4 8
 
6.1%
6 7
 
5.3%
3 7
 
5.3%
9 5
 
3.8%
7 4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43
32.8%
, 26
19.8%
5 10
 
7.6%
1 9
 
6.9%
2 9
 
6.9%
4 8
 
6.1%
6 7
 
5.3%
3 7
 
5.3%
9 5
 
3.8%
7 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43
32.8%
, 26
19.8%
5 10
 
7.6%
1 9
 
6.9%
2 9
 
6.9%
4 8
 
6.1%
6 7
 
5.3%
3 7
 
5.3%
9 5
 
3.8%
7 4
 
3.1%

ZL2
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
SO2
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSO2
2nd rowSO2
3rd rowSO2
4th rowSO2
5th rowSO2

Common Values

ValueCountFrequency (%)
SO2 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:01.817333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:01.925642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
so2 28
100.0%

Most occurring characters

ValueCountFrequency (%)
S 28
33.3%
O 28
33.3%
2 28
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 28
33.3%
O 28
33.3%
2 28
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 28
33.3%
O 28
33.3%
2 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 28
33.3%
O 28
33.3%
2 28
33.3%

MJ2
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
t/rok
28 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt/rok
2nd rowt/rok
3rd rowt/rok
4th rowt/rok
5th rowt/rok

Common Values

ValueCountFrequency (%)
t/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:02.059278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:02.245500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

MNO2
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:02.575267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5
Min length1

Characters and Unicode

Total characters140
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row0,204
2nd row630,381
3rd row39,962
4th row548,823
5th row0,076
ValueCountFrequency (%)
0,204 1
 
3.6%
0,188 1
 
3.6%
39,962 1
 
3.6%
548,823 1
 
3.6%
0,076 1
 
3.6%
0,045 1
 
3.6%
33,116 1
 
3.6%
3,18 1
 
3.6%
0,173 1
 
3.6%
0,114 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:03.005284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27
19.3%
, 27
19.3%
3 15
10.7%
1 14
10.0%
2 13
9.3%
4 10
 
7.1%
8 8
 
5.7%
6 8
 
5.7%
9 7
 
5.0%
7 6
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27
19.3%
, 27
19.3%
3 15
10.7%
1 14
10.0%
2 13
9.3%
4 10
 
7.1%
8 8
 
5.7%
6 8
 
5.7%
9 7
 
5.0%
7 6
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27
19.3%
, 27
19.3%
3 15
10.7%
1 14
10.0%
2 13
9.3%
4 10
 
7.1%
8 8
 
5.7%
6 8
 
5.7%
9 7
 
5.0%
7 6
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27
19.3%
, 27
19.3%
3 15
10.7%
1 14
10.0%
2 13
9.3%
4 10
 
7.1%
8 8
 
5.7%
6 8
 
5.7%
9 7
 
5.0%
7 6
 
4.3%

ZL3
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
NOx
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOx
2nd rowNOx
3rd rowNOx
4th rowNOx
5th rowNOx

Common Values

ValueCountFrequency (%)
NOx 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:03.184067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:03.305825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
nox 28
100.0%

Most occurring characters

ValueCountFrequency (%)
N 28
33.3%
O 28
33.3%
x 28
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28
33.3%
O 28
33.3%
x 28
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28
33.3%
O 28
33.3%
x 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28
33.3%
O 28
33.3%
x 28
33.3%

MJ3
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
t/rok
28 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt/rok
2nd rowt/rok
3rd rowt/rok
4th rowt/rok
5th rowt/rok

Common Values

ValueCountFrequency (%)
t/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:03.426775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:03.534259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

MNO3
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:03.733377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.6785714
Min length5

Characters and Unicode

Total characters159
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row3,662
2nd row522,117
3rd row307,607
4th row659,592
5th row2,178
ValueCountFrequency (%)
3,662 1
 
3.6%
2,831 1
 
3.6%
307,607 1
 
3.6%
659,592 1
 
3.6%
2,178 1
 
3.6%
1,952 1
 
3.6%
644,773 1
 
3.6%
47,142 1
 
3.6%
4,601 1
 
3.6%
3,856 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:04.191987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 28
17.6%
1 18
11.3%
7 18
11.3%
0 16
10.1%
6 15
9.4%
3 13
8.2%
2 13
8.2%
5 13
8.2%
4 10
 
6.3%
9 9
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 28
17.6%
1 18
11.3%
7 18
11.3%
0 16
10.1%
6 15
9.4%
3 13
8.2%
2 13
8.2%
5 13
8.2%
4 10
 
6.3%
9 9
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 28
17.6%
1 18
11.3%
7 18
11.3%
0 16
10.1%
6 15
9.4%
3 13
8.2%
2 13
8.2%
5 13
8.2%
4 10
 
6.3%
9 9
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 28
17.6%
1 18
11.3%
7 18
11.3%
0 16
10.1%
6 15
9.4%
3 13
8.2%
2 13
8.2%
5 13
8.2%
4 10
 
6.3%
9 9
 
5.7%

ZL4
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
CO
28 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO
2nd rowCO
3rd rowCO
4th rowCO
5th rowCO

Common Values

ValueCountFrequency (%)
CO 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:04.373380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:04.535836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
co 28
100.0%

Most occurring characters

ValueCountFrequency (%)
C 28
50.0%
O 28
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 28
50.0%
O 28
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 28
50.0%
O 28
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 28
50.0%
O 28
50.0%

MJ4
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
t/rok
28 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt/rok
2nd rowt/rok
3rd rowt/rok
4th rowt/rok
5th rowt/rok

Common Values

ValueCountFrequency (%)
t/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:04.705688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:04.874117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 28
20.0%
/ 28
20.0%
r 28
20.0%
o 28
20.0%
k 28
20.0%

MNO4
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:05.106981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.1785714
Min length1

Characters and Unicode

Total characters145
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,449
2nd row25,36
3rd row8,6
4th row1550,205
5th row0,252
ValueCountFrequency (%)
0,018 2
 
7.1%
25,36 1
 
3.6%
8,6 1
 
3.6%
1550,205 1
 
3.6%
0,252 1
 
3.6%
0,045 1
 
3.6%
2944,708 1
 
3.6%
6,417 1
 
3.6%
0,388 1
 
3.6%
0,343 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:05.695682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 27
18.6%
0 25
17.2%
1 14
9.7%
2 13
9.0%
5 13
9.0%
3 12
8.3%
6 11
7.6%
8 9
 
6.2%
7 8
 
5.5%
4 7
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 27
18.6%
0 25
17.2%
1 14
9.7%
2 13
9.0%
5 13
9.0%
3 12
8.3%
6 11
7.6%
8 9
 
6.2%
7 8
 
5.5%
4 7
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 27
18.6%
0 25
17.2%
1 14
9.7%
2 13
9.0%
5 13
9.0%
3 12
8.3%
6 11
7.6%
8 9
 
6.2%
7 8
 
5.5%
4 7
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 27
18.6%
0 25
17.2%
1 14
9.7%
2 13
9.0%
5 13
9.0%
3 12
8.3%
6 11
7.6%
8 9
 
6.2%
7 8
 
5.5%
4 7
 
4.8%

ZL5
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)7.1%
Missing10
Missing (%)26.3%
Memory size432.0 B
C
27 
Sb
 
1

Length

Max length2
Median length1
Mean length1.0357143
Min length1

Characters and Unicode

Total characters29
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowC
2nd rowSb
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 27
71.1%
Sb 1
 
2.6%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:05.935875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:06.114276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
c 27
96.4%
sb 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 27
93.1%
S 1
 
3.4%
b 1
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 27
93.1%
S 1
 
3.4%
b 1
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 27
93.1%
S 1
 
3.4%
b 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 27
93.1%
S 1
 
3.4%
b 1
 
3.4%

MJ5
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)7.1%
Missing10
Missing (%)26.3%
Memory size432.0 B
t/rok
27 
kg/rok
 
1

Length

Max length6
Median length5
Mean length5.0357143
Min length5

Characters and Unicode

Total characters141
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowt/rok
2nd rowkg/rok
3rd rowt/rok
4th rowt/rok
5th rowt/rok

Common Values

ValueCountFrequency (%)
t/rok 27
71.1%
kg/rok 1
 
2.6%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:06.302730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:06.428399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t/rok 27
96.4%
kg/rok 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
k 29
20.6%
/ 28
19.9%
r 28
19.9%
o 28
19.9%
t 27
19.1%
g 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 29
20.6%
/ 28
19.9%
r 28
19.9%
o 28
19.9%
t 27
19.1%
g 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 29
20.6%
/ 28
19.9%
r 28
19.9%
o 28
19.9%
t 27
19.1%
g 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 29
20.6%
/ 28
19.9%
r 28
19.9%
o 28
19.9%
t 27
19.1%
g 1
 
0.7%

MNO5
Text

Missing 

Distinct26
Distinct (%)92.9%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:06.646710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9285714
Min length1

Characters and Unicode

Total characters138
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row0,11
2nd row2,624
3rd row1,239
4th row20,368
5th row0,024
ValueCountFrequency (%)
0,015 2
 
7.1%
0,099 2
 
7.1%
1,239 1
 
3.6%
20,368 1
 
3.6%
0,024 1
 
3.6%
0,034 1
 
3.6%
32,603 1
 
3.6%
0,713 1
 
3.6%
0,127 1
 
3.6%
0,002 1
 
3.6%
Other values (16) 16
57.1%
2024-10-26T20:10:07.210757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40
29.0%
, 27
19.6%
2 13
 
9.4%
1 11
 
8.0%
9 9
 
6.5%
3 9
 
6.5%
6 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
8 5
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40
29.0%
, 27
19.6%
2 13
 
9.4%
1 11
 
8.0%
9 9
 
6.5%
3 9
 
6.5%
6 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
8 5
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40
29.0%
, 27
19.6%
2 13
 
9.4%
1 11
 
8.0%
9 9
 
6.5%
3 9
 
6.5%
6 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
8 5
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40
29.0%
, 27
19.6%
2 13
 
9.4%
1 11
 
8.0%
9 9
 
6.5%
3 9
 
6.5%
6 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
8 5
 
3.6%

ZL6
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Sb
18 
NH3
9 
As
 
1

Length

Max length3
Median length2
Mean length2.3214286
Min length2

Characters and Unicode

Total characters65
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowSb
2nd rowAs
3rd rowNH3
4th rowNH3
5th rowSb

Common Values

ValueCountFrequency (%)
Sb 18
47.4%
NH3 9
23.7%
As 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:07.458095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:07.604383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sb 18
64.3%
nh3 9
32.1%
as 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
S 18
27.7%
b 18
27.7%
N 9
13.8%
H 9
13.8%
3 9
13.8%
A 1
 
1.5%
s 1
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 18
27.7%
b 18
27.7%
N 9
13.8%
H 9
13.8%
3 9
13.8%
A 1
 
1.5%
s 1
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 18
27.7%
b 18
27.7%
N 9
13.8%
H 9
13.8%
3 9
13.8%
A 1
 
1.5%
s 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 18
27.7%
b 18
27.7%
N 9
13.8%
H 9
13.8%
3 9
13.8%
A 1
 
1.5%
s 1
 
1.5%

MJ6
Categorical

High correlation  Missing 

Distinct2
Distinct (%)7.1%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
19 
t/rok
9 

Length

Max length6
Median length6
Mean length5.6785714
Min length5

Characters and Unicode

Total characters159
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowt/rok
4th rowt/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 19
50.0%
t/rok 9
23.7%
(Missing) 10
26.3%

Length

2024-10-26T20:10:07.784273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:07.924389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 19
67.9%
t/rok 9
32.1%

Most occurring characters

ValueCountFrequency (%)
k 47
29.6%
/ 28
17.6%
r 28
17.6%
o 28
17.6%
g 19
11.9%
t 9
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 47
29.6%
/ 28
17.6%
r 28
17.6%
o 28
17.6%
g 19
11.9%
t 9
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 47
29.6%
/ 28
17.6%
r 28
17.6%
o 28
17.6%
g 19
11.9%
t 9
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 47
29.6%
/ 28
17.6%
r 28
17.6%
o 28
17.6%
g 19
11.9%
t 9
 
5.7%

MNO6
Text

Missing 

Distinct26
Distinct (%)92.9%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:08.153415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6428571
Min length1

Characters and Unicode

Total characters130
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row0,003
2nd row64,29
3rd row1,547
4th row3,074
5th row0,234
ValueCountFrequency (%)
0 2
 
7.1%
0,108 2
 
7.1%
1,547 1
 
3.6%
3,074 1
 
3.6%
0,234 1
 
3.6%
0,06 1
 
3.6%
28,258 1
 
3.6%
3,25 1
 
3.6%
0,002 1
 
3.6%
0,911 1
 
3.6%
Other values (16) 16
57.1%
2024-10-26T20:10:08.571934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34
26.2%
, 26
20.0%
2 15
11.5%
1 11
 
8.5%
4 9
 
6.9%
5 9
 
6.9%
3 7
 
5.4%
8 6
 
4.6%
7 5
 
3.8%
9 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34
26.2%
, 26
20.0%
2 15
11.5%
1 11
 
8.5%
4 9
 
6.9%
5 9
 
6.9%
3 7
 
5.4%
8 6
 
4.6%
7 5
 
3.8%
9 4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34
26.2%
, 26
20.0%
2 15
11.5%
1 11
 
8.5%
4 9
 
6.9%
5 9
 
6.9%
3 7
 
5.4%
8 6
 
4.6%
7 5
 
3.8%
9 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34
26.2%
, 26
20.0%
2 15
11.5%
1 11
 
8.5%
4 9
 
6.9%
5 9
 
6.9%
3 7
 
5.4%
8 6
 
4.6%
7 5
 
3.8%
9 4
 
3.1%

ZL7
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
As
18 
Sb
9 
Cd
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowAs
2nd rowCd
3rd rowSb
4th rowSb
5th rowAs

Common Values

ValueCountFrequency (%)
As 18
47.4%
Sb 9
23.7%
Cd 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:08.733993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:08.877468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
as 18
64.3%
sb 9
32.1%
cd 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
A 18
32.1%
s 18
32.1%
S 9
16.1%
b 9
16.1%
C 1
 
1.8%
d 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 18
32.1%
s 18
32.1%
S 9
16.1%
b 9
16.1%
C 1
 
1.8%
d 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 18
32.1%
s 18
32.1%
S 9
16.1%
b 9
16.1%
C 1
 
1.8%
d 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 18
32.1%
s 18
32.1%
S 9
16.1%
b 9
16.1%
C 1
 
1.8%
d 1
 
1.8%

MJ7
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:09.016395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:09.156303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO7
Text

Missing 

Distinct26
Distinct (%)92.9%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:09.393222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.3214286
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row0,013
2nd row3,936
3rd row0,653
4th row1,88
5th row0,123
ValueCountFrequency (%)
0 2
 
7.1%
0,1 2
 
7.1%
0,653 1
 
3.6%
1,88 1
 
3.6%
0,123 1
 
3.6%
0,09 1
 
3.6%
2,487 1
 
3.6%
3,25 1
 
3.6%
0,002 1
 
3.6%
0,054 1
 
3.6%
Other values (16) 16
57.1%
2024-10-26T20:10:09.874019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
25.6%
, 25
20.7%
1 15
12.4%
5 9
 
7.4%
3 8
 
6.6%
2 7
 
5.8%
6 6
 
5.0%
7 6
 
5.0%
4 5
 
4.1%
9 5
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31
25.6%
, 25
20.7%
1 15
12.4%
5 9
 
7.4%
3 8
 
6.6%
2 7
 
5.8%
6 6
 
5.0%
7 6
 
5.0%
4 5
 
4.1%
9 5
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31
25.6%
, 25
20.7%
1 15
12.4%
5 9
 
7.4%
3 8
 
6.6%
2 7
 
5.8%
6 6
 
5.0%
7 6
 
5.0%
4 5
 
4.1%
9 5
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31
25.6%
, 25
20.7%
1 15
12.4%
5 9
 
7.4%
3 8
 
6.6%
2 7
 
5.8%
6 6
 
5.0%
7 6
 
5.0%
4 5
 
4.1%
9 5
 
4.1%

ZL8
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Cd
18 
As
9 
Cr
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowCd
2nd rowCr
3rd rowAs
4th rowAs
5th rowCd

Common Values

ValueCountFrequency (%)
Cd 18
47.4%
As 9
23.7%
Cr 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:10.038509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:10.178825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cd 18
64.3%
as 9
32.1%
cr 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 19
33.9%
d 18
32.1%
A 9
16.1%
s 9
16.1%
r 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 19
33.9%
d 18
32.1%
A 9
16.1%
s 9
16.1%
r 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 19
33.9%
d 18
32.1%
A 9
16.1%
s 9
16.1%
r 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 19
33.9%
d 18
32.1%
A 9
16.1%
s 9
16.1%
r 1
 
1.8%

MJ8
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:10.324172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:10.444123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO8
Text

Missing 

Distinct25
Distinct (%)89.3%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:10.764043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1071429
Min length1

Characters and Unicode

Total characters115
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)82.1%

Sample

1st row0,003
2nd row83,315
3rd row0,331
4th row0,98
5th row0,025
ValueCountFrequency (%)
0 3
 
10.7%
0,1 2
 
7.1%
0,214 1
 
3.6%
0,331 1
 
3.6%
0,98 1
 
3.6%
0,025 1
 
3.6%
0,03 1
 
3.6%
1,287 1
 
3.6%
3,25 1
 
3.6%
0,109 1
 
3.6%
Other values (15) 15
53.6%
2024-10-26T20:10:11.150412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36
31.3%
, 24
20.9%
1 12
 
10.4%
3 10
 
8.7%
2 9
 
7.8%
7 7
 
6.1%
5 6
 
5.2%
4 3
 
2.6%
8 3
 
2.6%
9 3
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36
31.3%
, 24
20.9%
1 12
 
10.4%
3 10
 
8.7%
2 9
 
7.8%
7 7
 
6.1%
5 6
 
5.2%
4 3
 
2.6%
8 3
 
2.6%
9 3
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36
31.3%
, 24
20.9%
1 12
 
10.4%
3 10
 
8.7%
2 9
 
7.8%
7 7
 
6.1%
5 6
 
5.2%
4 3
 
2.6%
8 3
 
2.6%
9 3
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36
31.3%
, 24
20.9%
1 12
 
10.4%
3 10
 
8.7%
2 9
 
7.8%
7 7
 
6.1%
5 6
 
5.2%
4 3
 
2.6%
8 3
 
2.6%
9 3
 
2.6%

ZL9
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Cr
18 
Cd
9 
Hg
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowCr
2nd rowHg
3rd rowCd
4th rowCd
5th rowCr

Common Values

ValueCountFrequency (%)
Cr 18
47.4%
Cd 9
23.7%
Hg 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:11.303344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:11.425525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cr 18
64.3%
cd 9
32.1%
hg 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 27
48.2%
r 18
32.1%
d 9
 
16.1%
H 1
 
1.8%
g 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 27
48.2%
r 18
32.1%
d 9
 
16.1%
H 1
 
1.8%
g 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 27
48.2%
r 18
32.1%
d 9
 
16.1%
H 1
 
1.8%
g 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 27
48.2%
r 18
32.1%
d 9
 
16.1%
H 1
 
1.8%
g 1
 
1.8%

MJ9
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:11.555550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:11.684150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO9
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:11.884353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4642857
Min length1

Characters and Unicode

Total characters125
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row0,043
2nd row20,337
3rd row0,057
4th row0,11
5th row0,037
ValueCountFrequency (%)
0,043 1
 
3.6%
0,087 1
 
3.6%
0,057 1
 
3.6%
0,11 1
 
3.6%
0,037 1
 
3.6%
0,41 1
 
3.6%
0,176 1
 
3.6%
3,25 1
 
3.6%
0,029 1
 
3.6%
0,042 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:12.344061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
25.6%
, 26
20.8%
1 16
12.8%
3 11
 
8.8%
2 8
 
6.4%
4 7
 
5.6%
7 6
 
4.8%
6 6
 
4.8%
8 5
 
4.0%
9 4
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32
25.6%
, 26
20.8%
1 16
12.8%
3 11
 
8.8%
2 8
 
6.4%
4 7
 
5.6%
7 6
 
4.8%
6 6
 
4.8%
8 5
 
4.0%
9 4
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32
25.6%
, 26
20.8%
1 16
12.8%
3 11
 
8.8%
2 8
 
6.4%
4 7
 
5.6%
7 6
 
4.8%
6 6
 
4.8%
8 5
 
4.0%
9 4
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32
25.6%
, 26
20.8%
1 16
12.8%
3 11
 
8.8%
2 8
 
6.4%
4 7
 
5.6%
7 6
 
4.8%
6 6
 
4.8%
8 5
 
4.0%
9 4
 
3.2%

ZL10
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Hg
18 
Cr
9 
Co
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowHg
2nd rowCo
3rd rowCr
4th rowCr
5th rowHg

Common Values

ValueCountFrequency (%)
Hg 18
47.4%
Cr 9
23.7%
Co 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:12.497032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:12.618610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hg 18
64.3%
cr 9
32.1%
co 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
H 18
32.1%
g 18
32.1%
C 10
17.9%
r 9
16.1%
o 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 18
32.1%
g 18
32.1%
C 10
17.9%
r 9
16.1%
o 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 18
32.1%
g 18
32.1%
C 10
17.9%
r 9
16.1%
o 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 18
32.1%
g 18
32.1%
C 10
17.9%
r 9
16.1%
o 1
 
1.8%

MJ10
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:12.746040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:12.864465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO10
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:13.035285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3214286
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,442
2nd row36,767
3rd row1,785
4th row1,81
5th row0,025
ValueCountFrequency (%)
0 2
 
7.1%
36,767 1
 
3.6%
1,785 1
 
3.6%
1,81 1
 
3.6%
0,025 1
 
3.6%
0,049 1
 
3.6%
0,999 1
 
3.6%
3,53 1
 
3.6%
0,039 1
 
3.6%
0,019 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:13.426245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27
22.3%
, 25
20.7%
1 11
9.1%
2 10
 
8.3%
9 10
 
8.3%
3 8
 
6.6%
5 7
 
5.8%
4 6
 
5.0%
6 6
 
5.0%
8 6
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27
22.3%
, 25
20.7%
1 11
9.1%
2 10
 
8.3%
9 10
 
8.3%
3 8
 
6.6%
5 7
 
5.8%
4 6
 
5.0%
6 6
 
5.0%
8 6
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27
22.3%
, 25
20.7%
1 11
9.1%
2 10
 
8.3%
9 10
 
8.3%
3 8
 
6.6%
5 7
 
5.8%
4 6
 
5.0%
6 6
 
5.0%
8 6
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27
22.3%
, 25
20.7%
1 11
9.1%
2 10
 
8.3%
9 10
 
8.3%
3 8
 
6.6%
5 7
 
5.8%
4 6
 
5.0%
6 6
 
5.0%
8 6
 
5.0%

ZL11
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Co
18 
Hg
9 
Mn
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowCo
2nd rowMn
3rd rowHg
4th rowHg
5th rowCo

Common Values

ValueCountFrequency (%)
Co 18
47.4%
Hg 9
23.7%
Mn 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:13.624072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:13.765776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
co 18
64.3%
hg 9
32.1%
mn 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 18
32.1%
o 18
32.1%
H 9
16.1%
g 9
16.1%
M 1
 
1.8%
n 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 18
32.1%
o 18
32.1%
H 9
16.1%
g 9
16.1%
M 1
 
1.8%
n 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 18
32.1%
o 18
32.1%
H 9
16.1%
g 9
16.1%
M 1
 
1.8%
n 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 18
32.1%
o 18
32.1%
H 9
16.1%
g 9
16.1%
M 1
 
1.8%
n 1
 
1.8%

MJ11
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:13.926645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:14.055303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO11
Text

Missing 

Distinct25
Distinct (%)89.3%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:14.271884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length4.3214286
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)78.6%

Sample

1st row0,003
2nd row1036,515
3rd row0,41
4th row1,56
5th row0,05
ValueCountFrequency (%)
0,003 2
 
7.1%
0,011 2
 
7.1%
0 2
 
7.1%
0,044 1
 
3.6%
0,32 1
 
3.6%
19,539 1
 
3.6%
3,25 1
 
3.6%
0,002 1
 
3.6%
0,022 1
 
3.6%
0,911 1
 
3.6%
Other values (15) 15
53.6%
2024-10-26T20:10:14.684347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
26.4%
, 25
20.7%
1 19
15.7%
2 11
 
9.1%
3 8
 
6.6%
5 8
 
6.6%
4 5
 
4.1%
6 4
 
3.3%
9 4
 
3.3%
7 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32
26.4%
, 25
20.7%
1 19
15.7%
2 11
 
9.1%
3 8
 
6.6%
5 8
 
6.6%
4 5
 
4.1%
6 4
 
3.3%
9 4
 
3.3%
7 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32
26.4%
, 25
20.7%
1 19
15.7%
2 11
 
9.1%
3 8
 
6.6%
5 8
 
6.6%
4 5
 
4.1%
6 4
 
3.3%
9 4
 
3.3%
7 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32
26.4%
, 25
20.7%
1 19
15.7%
2 11
 
9.1%
3 8
 
6.6%
5 8
 
6.6%
4 5
 
4.1%
6 4
 
3.3%
9 4
 
3.3%
7 4
 
3.3%

ZL12
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Mn
18 
Co
9 
Cu
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowMn
2nd rowCu
3rd rowCo
4th rowCo
5th rowMn

Common Values

ValueCountFrequency (%)
Mn 18
47.4%
Co 9
23.7%
Cu 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:14.845252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:14.967000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mn 18
64.3%
co 9
32.1%
cu 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
M 18
32.1%
n 18
32.1%
C 10
17.9%
o 9
16.1%
u 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 18
32.1%
n 18
32.1%
C 10
17.9%
o 9
16.1%
u 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 18
32.1%
n 18
32.1%
C 10
17.9%
o 9
16.1%
u 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 18
32.1%
n 18
32.1%
C 10
17.9%
o 9
16.1%
u 1
 
1.8%

MJ12
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:15.095149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:15.218450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO12
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:15.399213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1785714
Min length1

Characters and Unicode

Total characters117
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,066
2nd row28,865
3rd row0,021
4th row0,41
5th row4,51
ValueCountFrequency (%)
0 2
 
7.1%
28,865 1
 
3.6%
0,021 1
 
3.6%
0,41 1
 
3.6%
4,51 1
 
3.6%
0,64 1
 
3.6%
0,508 1
 
3.6%
3,25 1
 
3.6%
0,009 1
 
3.6%
3,356 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:15.784083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 25
21.4%
0 23
19.7%
1 13
11.1%
2 10
 
8.5%
3 10
 
8.5%
6 9
 
7.7%
5 8
 
6.8%
7 6
 
5.1%
4 6
 
5.1%
8 4
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 25
21.4%
0 23
19.7%
1 13
11.1%
2 10
 
8.5%
3 10
 
8.5%
6 9
 
7.7%
5 8
 
6.8%
7 6
 
5.1%
4 6
 
5.1%
8 4
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 25
21.4%
0 23
19.7%
1 13
11.1%
2 10
 
8.5%
3 10
 
8.5%
6 9
 
7.7%
5 8
 
6.8%
7 6
 
5.1%
4 6
 
5.1%
8 4
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 25
21.4%
0 23
19.7%
1 13
11.1%
2 10
 
8.5%
3 10
 
8.5%
6 9
 
7.7%
5 8
 
6.8%
7 6
 
5.1%
4 6
 
5.1%
8 4
 
3.4%

ZL13
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Cu
18 
Mn
9 
Ni
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowCu
2nd rowNi
3rd rowMn
4th rowMn
5th rowCu

Common Values

ValueCountFrequency (%)
Cu 18
47.4%
Mn 9
23.7%
Ni 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:15.935661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:16.057932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cu 18
64.3%
mn 9
32.1%
ni 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
C 18
32.1%
u 18
32.1%
M 9
16.1%
n 9
16.1%
N 1
 
1.8%
i 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 18
32.1%
u 18
32.1%
M 9
16.1%
n 9
16.1%
N 1
 
1.8%
i 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 18
32.1%
u 18
32.1%
M 9
16.1%
n 9
16.1%
N 1
 
1.8%
i 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 18
32.1%
u 18
32.1%
M 9
16.1%
n 9
16.1%
N 1
 
1.8%
i 1
 
1.8%

MJ13
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:16.194021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:16.314067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO13
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:16.538388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.5
Min length1

Characters and Unicode

Total characters126
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row0,021
2nd row43,297
3rd row0,651
4th row1,64
5th row0,197
ValueCountFrequency (%)
0,021 1
 
3.6%
0,454 1
 
3.6%
0,651 1
 
3.6%
1,64 1
 
3.6%
0,197 1
 
3.6%
2,06 1
 
3.6%
1,643 1
 
3.6%
16,95 1
 
3.6%
0,004 1
 
3.6%
0,194 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:16.955686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
20.6%
0 23
18.3%
1 17
13.5%
6 13
10.3%
2 11
8.7%
4 10
 
7.9%
5 9
 
7.1%
3 6
 
4.8%
9 5
 
4.0%
7 4
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 26
20.6%
0 23
18.3%
1 17
13.5%
6 13
10.3%
2 11
8.7%
4 10
 
7.9%
5 9
 
7.1%
3 6
 
4.8%
9 5
 
4.0%
7 4
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 26
20.6%
0 23
18.3%
1 17
13.5%
6 13
10.3%
2 11
8.7%
4 10
 
7.9%
5 9
 
7.1%
3 6
 
4.8%
9 5
 
4.0%
7 4
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 26
20.6%
0 23
18.3%
1 17
13.5%
6 13
10.3%
2 11
8.7%
4 10
 
7.9%
5 9
 
7.1%
3 6
 
4.8%
9 5
 
4.0%
7 4
 
3.2%

ZL14
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Ni
18 
Cu
9 
Pb
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowNi
2nd rowPb
3rd rowCu
4th rowCu
5th rowNi

Common Values

ValueCountFrequency (%)
Ni 18
47.4%
Cu 9
23.7%
Pb 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:17.115087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:17.241049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ni 18
64.3%
cu 9
32.1%
pb 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
N 18
32.1%
i 18
32.1%
C 9
16.1%
u 9
16.1%
P 1
 
1.8%
b 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 18
32.1%
i 18
32.1%
C 9
16.1%
u 9
16.1%
P 1
 
1.8%
b 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 18
32.1%
i 18
32.1%
C 9
16.1%
u 9
16.1%
P 1
 
1.8%
b 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 18
32.1%
i 18
32.1%
C 9
16.1%
u 9
16.1%
P 1
 
1.8%
b 1
 
1.8%

MJ14
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:17.377692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:17.495780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO14
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:17.715183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4642857
Min length1

Characters and Unicode

Total characters125
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row0,025
2nd row36,737
3rd row6,616
4th row0,78
5th row0,049
ValueCountFrequency (%)
0,025 1
 
3.6%
0,725 1
 
3.6%
6,616 1
 
3.6%
0,78 1
 
3.6%
0,049 1
 
3.6%
0,26 1
 
3.6%
1,043 1
 
3.6%
37,76 1
 
3.6%
0,006 1
 
3.6%
0,012 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:18.297711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
20.8%
0 25
20.0%
6 12
9.6%
7 11
8.8%
1 11
8.8%
2 10
 
8.0%
3 8
 
6.4%
4 8
 
6.4%
9 6
 
4.8%
5 5
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 26
20.8%
0 25
20.0%
6 12
9.6%
7 11
8.8%
1 11
8.8%
2 10
 
8.0%
3 8
 
6.4%
4 8
 
6.4%
9 6
 
4.8%
5 5
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 26
20.8%
0 25
20.0%
6 12
9.6%
7 11
8.8%
1 11
8.8%
2 10
 
8.0%
3 8
 
6.4%
4 8
 
6.4%
9 6
 
4.8%
5 5
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 26
20.8%
0 25
20.0%
6 12
9.6%
7 11
8.8%
1 11
8.8%
2 10
 
8.0%
3 8
 
6.4%
4 8
 
6.4%
9 6
 
4.8%
5 5
 
4.0%

ZL15
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Pb
18 
Ni
9 
V
 
1

Length

Max length2
Median length2
Mean length1.9642857
Min length1

Characters and Unicode

Total characters55
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowPb
2nd rowV
3rd rowNi
4th rowNi
5th rowPb

Common Values

ValueCountFrequency (%)
Pb 18
47.4%
Ni 9
23.7%
V 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:18.463985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:18.590256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pb 18
64.3%
ni 9
32.1%
v 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
P 18
32.7%
b 18
32.7%
N 9
16.4%
i 9
16.4%
V 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 18
32.7%
b 18
32.7%
N 9
16.4%
i 9
16.4%
V 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 18
32.7%
b 18
32.7%
N 9
16.4%
i 9
16.4%
V 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 18
32.7%
b 18
32.7%
N 9
16.4%
i 9
16.4%
V 1
 
1.8%

MJ15
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:18.751224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:18.884045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO15
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:19.072791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3214286
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,016
2nd row27,553
3rd row1,246
4th row0,62
5th row0,16
ValueCountFrequency (%)
0 2
 
7.1%
27,553 1
 
3.6%
1,246 1
 
3.6%
0,62 1
 
3.6%
0,16 1
 
3.6%
0,37 1
 
3.6%
0,626 1
 
3.6%
3,25 1
 
3.6%
0,008 1
 
3.6%
0,104 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:19.490207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30
24.8%
, 25
20.7%
1 16
13.2%
2 10
 
8.3%
3 9
 
7.4%
6 8
 
6.6%
5 8
 
6.6%
7 6
 
5.0%
9 4
 
3.3%
4 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30
24.8%
, 25
20.7%
1 16
13.2%
2 10
 
8.3%
3 9
 
7.4%
6 8
 
6.6%
5 8
 
6.6%
7 6
 
5.0%
9 4
 
3.3%
4 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30
24.8%
, 25
20.7%
1 16
13.2%
2 10
 
8.3%
3 9
 
7.4%
6 8
 
6.6%
5 8
 
6.6%
7 6
 
5.0%
9 4
 
3.3%
4 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30
24.8%
, 25
20.7%
1 16
13.2%
2 10
 
8.3%
3 9
 
7.4%
6 8
 
6.6%
5 8
 
6.6%
7 6
 
5.0%
9 4
 
3.3%
4 4
 
3.3%

ZL16
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
V
18 
Pb
9 
Zn
 
1

Length

Max length2
Median length1
Mean length1.3571429
Min length1

Characters and Unicode

Total characters38
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowV
2nd rowZn
3rd rowPb
4th rowPb
5th rowV

Common Values

ValueCountFrequency (%)
V 18
47.4%
Pb 9
23.7%
Zn 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:19.675389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:19.835788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
v 18
64.3%
pb 9
32.1%
zn 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
V 18
47.4%
P 9
23.7%
b 9
23.7%
Z 1
 
2.6%
n 1
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 18
47.4%
P 9
23.7%
b 9
23.7%
Z 1
 
2.6%
n 1
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 18
47.4%
P 9
23.7%
b 9
23.7%
Z 1
 
2.6%
n 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 18
47.4%
P 9
23.7%
b 9
23.7%
Z 1
 
2.6%
n 1
 
2.6%

MJ16
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:20.034031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:20.238567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO16
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:20.485908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3928571
Min length1

Characters and Unicode

Total characters123
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,007
2nd row176,47
3rd row4,249
4th row1,39
5th row0,025
ValueCountFrequency (%)
0 2
 
7.1%
176,47 1
 
3.6%
4,249 1
 
3.6%
1,39 1
 
3.6%
0,025 1
 
3.6%
0,13 1
 
3.6%
1,287 1
 
3.6%
3,25 1
 
3.6%
0,002 1
 
3.6%
0,108 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:20.981878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
26.0%
, 25
20.3%
1 19
15.4%
2 11
 
8.9%
5 8
 
6.5%
7 6
 
4.9%
6 6
 
4.9%
3 5
 
4.1%
9 4
 
3.3%
8 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32
26.0%
, 25
20.3%
1 19
15.4%
2 11
 
8.9%
5 8
 
6.5%
7 6
 
4.9%
6 6
 
4.9%
3 5
 
4.1%
9 4
 
3.3%
8 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32
26.0%
, 25
20.3%
1 19
15.4%
2 11
 
8.9%
5 8
 
6.5%
7 6
 
4.9%
6 6
 
4.9%
3 5
 
4.1%
9 4
 
3.3%
8 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32
26.0%
, 25
20.3%
1 19
15.4%
2 11
 
8.9%
5 8
 
6.5%
7 6
 
4.9%
6 6
 
4.9%
3 5
 
4.1%
9 4
 
3.3%
8 4
 
3.3%

ZL17
Categorical

High correlation  Missing 

Distinct2
Distinct (%)7.1%
Missing10
Missing (%)26.3%
Memory size432.0 B
Tl
19 
V
9 

Length

Max length2
Median length2
Mean length1.6785714
Min length1

Characters and Unicode

Total characters47
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTl
2nd rowTl
3rd rowV
4th rowV
5th rowTl

Common Values

ValueCountFrequency (%)
Tl 19
50.0%
V 9
23.7%
(Missing) 10
26.3%

Length

2024-10-26T20:10:21.234331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:21.405155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tl 19
67.9%
v 9
32.1%

Most occurring characters

ValueCountFrequency (%)
T 19
40.4%
l 19
40.4%
V 9
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 19
40.4%
l 19
40.4%
V 9
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 19
40.4%
l 19
40.4%
V 9
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 19
40.4%
l 19
40.4%
V 9
19.1%

MJ17
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:21.605606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:21.805052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO17
Text

Missing 

Distinct24
Distinct (%)85.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:22.084173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3214286
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)71.4%

Sample

1st row0,002
2nd row24,929
3rd row0,048
4th row0,31
5th row0,234
ValueCountFrequency (%)
0,002 2
 
7.1%
0,108 2
 
7.1%
0,2 2
 
7.1%
0 2
 
7.1%
3,095 1
 
3.6%
0,048 1
 
3.6%
0,31 1
 
3.6%
0,234 1
 
3.6%
0,06 1
 
3.6%
0,365 1
 
3.6%
Other values (14) 14
50.0%
2024-10-26T20:10:22.514282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
32.2%
, 25
20.7%
2 11
 
9.1%
1 8
 
6.6%
9 8
 
6.6%
3 7
 
5.8%
5 7
 
5.8%
4 6
 
5.0%
8 4
 
3.3%
6 3
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39
32.2%
, 25
20.7%
2 11
 
9.1%
1 8
 
6.6%
9 8
 
6.6%
3 7
 
5.8%
5 7
 
5.8%
4 6
 
5.0%
8 4
 
3.3%
6 3
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39
32.2%
, 25
20.7%
2 11
 
9.1%
1 8
 
6.6%
9 8
 
6.6%
3 7
 
5.8%
5 7
 
5.8%
4 6
 
5.0%
8 4
 
3.3%
6 3
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39
32.2%
, 25
20.7%
2 11
 
9.1%
1 8
 
6.6%
9 8
 
6.6%
3 7
 
5.8%
5 7
 
5.8%
4 6
 
5.0%
8 4
 
3.3%
6 3
 
2.5%

ZL18
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
F
19 
Tl
8 
Zn
 
1

Length

Max length2
Median length1
Mean length1.3214286
Min length1

Characters and Unicode

Total characters37
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowF
2nd rowF
3rd rowZn
4th rowTl
5th rowF

Common Values

ValueCountFrequency (%)
F 19
50.0%
Tl 8
21.1%
Zn 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:22.713343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:22.854051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 19
67.9%
tl 8
28.6%
zn 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
F 19
51.4%
T 8
21.6%
l 8
21.6%
Z 1
 
2.7%
n 1
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 19
51.4%
T 8
21.6%
l 8
21.6%
Z 1
 
2.7%
n 1
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 19
51.4%
T 8
21.6%
l 8
21.6%
Z 1
 
2.7%
n 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 19
51.4%
T 8
21.6%
l 8
21.6%
Z 1
 
2.7%
n 1
 
2.7%

MJ18
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:22.982128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:23.111799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO18
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:23.306011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.2142857
Min length1

Characters and Unicode

Total characters118
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row9,4
2nd row276,841
3rd row30,91
4th row1,93
5th row2,47
ValueCountFrequency (%)
1 2
 
7.1%
4,584 1
 
3.6%
30,91 1
 
3.6%
1,93 1
 
3.6%
2,47 1
 
3.6%
2,628 1
 
3.6%
79,961 1
 
3.6%
3 1
 
3.6%
6,944 1
 
3.6%
55,748 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:23.705648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 23
19.5%
1 15
12.7%
4 13
11.0%
0 13
11.0%
2 11
9.3%
6 9
 
7.6%
7 9
 
7.6%
9 8
 
6.8%
3 7
 
5.9%
5 5
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 23
19.5%
1 15
12.7%
4 13
11.0%
0 13
11.0%
2 11
9.3%
6 9
 
7.6%
7 9
 
7.6%
9 8
 
6.8%
3 7
 
5.9%
5 5
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 23
19.5%
1 15
12.7%
4 13
11.0%
0 13
11.0%
2 11
9.3%
6 9
 
7.6%
7 9
 
7.6%
9 8
 
6.8%
3 7
 
5.9%
5 5
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 23
19.5%
1 15
12.7%
4 13
11.0%
0 13
11.0%
2 11
9.3%
6 9
 
7.6%
7 9
 
7.6%
9 8
 
6.8%
3 7
 
5.9%
5 5
 
4.2%

ZL19
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
Cl
19 
F
8 
Tl
 
1

Length

Max length2
Median length2
Mean length1.7142857
Min length1

Characters and Unicode

Total characters48
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowCl
2nd rowCl
3rd rowTl
4th rowF
5th rowCl

Common Values

ValueCountFrequency (%)
Cl 19
50.0%
F 8
21.1%
Tl 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:23.873392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:23.991512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cl 19
67.9%
f 8
28.6%
tl 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
l 20
41.7%
C 19
39.6%
F 8
 
16.7%
T 1
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 20
41.7%
C 19
39.6%
F 8
 
16.7%
T 1
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 20
41.7%
C 19
39.6%
F 8
 
16.7%
T 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 20
41.7%
C 19
39.6%
F 8
 
16.7%
T 1
 
2.1%

MJ19
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing10
Missing (%)26.3%
Memory size432.0 B
kg/rok
28 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/rok
2nd rowkg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowkg/rok

Common Values

ValueCountFrequency (%)
kg/rok 28
73.7%
(Missing) 10
 
26.3%

Length

2024-10-26T20:10:24.125665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:24.235179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/rok 28
100.0%

Most occurring characters

ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 56
33.3%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%

MNO19
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:24.445330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.25
Min length1

Characters and Unicode

Total characters119
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row58,7
2nd row1280,063
3rd row0,018
4th row66
5th row61,18
ValueCountFrequency (%)
58,7 1
 
3.6%
32,356 1
 
3.6%
0,018 1
 
3.6%
66 1
 
3.6%
61,18 1
 
3.6%
59 1
 
3.6%
36 1
 
3.6%
47,013 1
 
3.6%
9 1
 
3.6%
36,894 1
 
3.6%
Other values (18) 18
64.3%
2024-10-26T20:10:24.946533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 18
15.1%
1 18
15.1%
8 12
10.1%
3 12
10.1%
0 11
9.2%
6 10
8.4%
7 9
7.6%
9 9
7.6%
2 8
6.7%
4 7
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 18
15.1%
1 18
15.1%
8 12
10.1%
3 12
10.1%
0 11
9.2%
6 10
8.4%
7 9
7.6%
9 9
7.6%
2 8
6.7%
4 7
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 18
15.1%
1 18
15.1%
8 12
10.1%
3 12
10.1%
0 11
9.2%
6 10
8.4%
7 9
7.6%
9 9
7.6%
2 8
6.7%
4 7
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 18
15.1%
1 18
15.1%
8 12
10.1%
3 12
10.1%
0 11
9.2%
6 10
8.4%
7 9
7.6%
9 9
7.6%
2 8
6.7%
4 7
 
5.9%

ZL20
Categorical

High correlation  Missing 

Distinct3
Distinct (%)10.7%
Missing10
Missing (%)26.3%
Memory size432.0 B
PCDD+PCDF
19 
Cl
8 
F
 
1

Length

Max length9
Median length9
Mean length6.7142857
Min length1

Characters and Unicode

Total characters188
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st rowPCDD+PCDF
2nd rowPCDD+PCDF
3rd rowF
4th rowCl
5th rowPCDD+PCDF

Common Values

ValueCountFrequency (%)
PCDD+PCDF 19
50.0%
Cl 8
21.1%
F 1
 
2.6%
(Missing) 10
26.3%

Length

2024-10-26T20:10:25.185672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:25.373106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pcdd+pcdf 19
67.9%
cl 8
28.6%
f 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
D 57
30.3%
C 46
24.5%
P 38
20.2%
F 20
 
10.6%
+ 19
 
10.1%
l 8
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 57
30.3%
C 46
24.5%
P 38
20.2%
F 20
 
10.6%
+ 19
 
10.1%
l 8
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 57
30.3%
C 46
24.5%
P 38
20.2%
F 20
 
10.6%
+ 19
 
10.1%
l 8
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 57
30.3%
C 46
24.5%
P 38
20.2%
F 20
 
10.6%
+ 19
 
10.1%
l 8
 
4.3%

MJ20
Categorical

High correlation  Missing 

Distinct2
Distinct (%)7.1%
Missing10
Missing (%)26.3%
Memory size432.0 B
mg/rok
19 
kg/rok
9 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmg/rok
2nd rowmg/rok
3rd rowkg/rok
4th rowkg/rok
5th rowmg/rok

Common Values

ValueCountFrequency (%)
mg/rok 19
50.0%
kg/rok 9
23.7%
(Missing) 10
26.3%

Length

2024-10-26T20:10:25.525576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:25.675881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mg/rok 19
67.9%
kg/rok 9
32.1%

Most occurring characters

ValueCountFrequency (%)
k 37
22.0%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%
m 19
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 37
22.0%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%
m 19
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 37
22.0%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%
m 19
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 37
22.0%
g 28
16.7%
/ 28
16.7%
r 28
16.7%
o 28
16.7%
m 19
11.3%

MNO20
Text

Missing 

Distinct27
Distinct (%)96.4%
Missing10
Missing (%)26.3%
Memory size432.0 B
2024-10-26T20:10:25.984032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.0357143
Min length1

Characters and Unicode

Total characters113
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row0,27
2nd row50,251
3rd row130
4th row553
5th row0,097
ValueCountFrequency (%)
0,27 2
 
7.1%
50,251 1
 
3.6%
130 1
 
3.6%
553 1
 
3.6%
0,097 1
 
3.6%
0,134 1
 
3.6%
3414 1
 
3.6%
5,8 1
 
3.6%
0,495 1
 
3.6%
5 1
 
3.6%
Other values (17) 17
60.7%
2024-10-26T20:10:26.354172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24
21.2%
, 20
17.7%
2 10
8.8%
5 10
8.8%
1 10
8.8%
9 10
8.8%
3 9
 
8.0%
4 8
 
7.1%
7 5
 
4.4%
6 4
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24
21.2%
, 20
17.7%
2 10
8.8%
5 10
8.8%
1 10
8.8%
9 10
8.8%
3 9
 
8.0%
4 8
 
7.1%
7 5
 
4.4%
6 4
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24
21.2%
, 20
17.7%
2 10
8.8%
5 10
8.8%
1 10
8.8%
9 10
8.8%
3 9
 
8.0%
4 8
 
7.1%
7 5
 
4.4%
6 4
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24
21.2%
, 20
17.7%
2 10
8.8%
5 10
8.8%
1 10
8.8%
9 10
8.8%
3 9
 
8.0%
4 8
 
7.1%
7 5
 
4.4%
6 4
 
3.5%

ZL21
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing29
Missing (%)76.3%
Memory size432.0 B
PCDD+PCDF
8 
Cl
1 

Length

Max length9
Median length9
Mean length8.2222222
Min length2

Characters and Unicode

Total characters74
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st rowCl
2nd rowPCDD+PCDF
3rd rowPCDD+PCDF
4th rowPCDD+PCDF
5th rowPCDD+PCDF

Common Values

ValueCountFrequency (%)
PCDD+PCDF 8
 
21.1%
Cl 1
 
2.6%
(Missing) 29
76.3%

Length

2024-10-26T20:10:26.514205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:26.658688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pcdd+pcdf 8
88.9%
cl 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
D 24
32.4%
C 17
23.0%
P 16
21.6%
+ 8
 
10.8%
F 8
 
10.8%
l 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 24
32.4%
C 17
23.0%
P 16
21.6%
+ 8
 
10.8%
F 8
 
10.8%
l 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 24
32.4%
C 17
23.0%
P 16
21.6%
+ 8
 
10.8%
F 8
 
10.8%
l 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 24
32.4%
C 17
23.0%
P 16
21.6%
+ 8
 
10.8%
F 8
 
10.8%
l 1
 
1.4%

MJ21
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing29
Missing (%)76.3%
Memory size432.0 B
mg/rok
8 
kg/rok
1 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters54
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st rowkg/rok
2nd rowmg/rok
3rd rowmg/rok
4th rowmg/rok
5th rowmg/rok

Common Values

ValueCountFrequency (%)
mg/rok 8
 
21.1%
kg/rok 1
 
2.6%
(Missing) 29
76.3%

Length

2024-10-26T20:10:26.787431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:26.918546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mg/rok 8
88.9%
kg/rok 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
k 10
18.5%
g 9
16.7%
/ 9
16.7%
r 9
16.7%
o 9
16.7%
m 8
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 10
18.5%
g 9
16.7%
/ 9
16.7%
r 9
16.7%
o 9
16.7%
m 8
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 10
18.5%
g 9
16.7%
/ 9
16.7%
r 9
16.7%
o 9
16.7%
m 8
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 10
18.5%
g 9
16.7%
/ 9
16.7%
r 9
16.7%
o 9
16.7%
m 8
14.8%

MNO21
Text

Missing 

Distinct9
Distinct (%)100.0%
Missing29
Missing (%)76.3%
Memory size432.0 B
2024-10-26T20:10:27.084247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.5555556
Min length1

Characters and Unicode

Total characters32
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st row10804
2nd row10
3rd row13,186
4th row3,3
5th row3,6
ValueCountFrequency (%)
10804 1
11.1%
10 1
11.1%
13,186 1
11.1%
3,3 1
11.1%
3,6 1
11.1%
3 1
11.1%
30,11 1
11.1%
21,6 1
11.1%
9,1 1
11.1%
2024-10-26T20:10:27.433225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8
25.0%
3 6
18.8%
, 6
18.8%
0 4
12.5%
6 3
 
9.4%
8 2
 
6.2%
4 1
 
3.1%
2 1
 
3.1%
9 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8
25.0%
3 6
18.8%
, 6
18.8%
0 4
12.5%
6 3
 
9.4%
8 2
 
6.2%
4 1
 
3.1%
2 1
 
3.1%
9 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8
25.0%
3 6
18.8%
, 6
18.8%
0 4
12.5%
6 3
 
9.4%
8 2
 
6.2%
4 1
 
3.1%
2 1
 
3.1%
9 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8
25.0%
3 6
18.8%
, 6
18.8%
0 4
12.5%
6 3
 
9.4%
8 2
 
6.2%
4 1
 
3.1%
2 1
 
3.1%
9 1
 
3.1%

ZL22
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing37
Missing (%)97.4%
Memory size432.0 B
2024-10-26T20:10:27.639506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPCDD+PCDF
ValueCountFrequency (%)
pcdd+pcdf 1
100.0%
2024-10-26T20:10:28.034050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 3
33.3%
P 2
22.2%
C 2
22.2%
+ 1
 
11.1%
F 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 3
33.3%
P 2
22.2%
C 2
22.2%
+ 1
 
11.1%
F 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 3
33.3%
P 2
22.2%
C 2
22.2%
+ 1
 
11.1%
F 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 3
33.3%
P 2
22.2%
C 2
22.2%
+ 1
 
11.1%
F 1
 
11.1%

MJ22
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing37
Missing (%)97.4%
Memory size432.0 B
2024-10-26T20:10:28.236975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowmg/rok
ValueCountFrequency (%)
mg/rok 1
100.0%
2024-10-26T20:10:28.548609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 1
16.7%
g 1
16.7%
/ 1
16.7%
r 1
16.7%
o 1
16.7%
k 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1
16.7%
g 1
16.7%
/ 1
16.7%
r 1
16.7%
o 1
16.7%
k 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1
16.7%
g 1
16.7%
/ 1
16.7%
r 1
16.7%
o 1
16.7%
k 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1
16.7%
g 1
16.7%
/ 1
16.7%
r 1
16.7%
o 1
16.7%
k 1
16.7%

MNO22
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing37
Missing (%)97.4%
Memory size432.0 B
6.0
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row6.0

Common Values

ValueCountFrequency (%)
6.0 1
 
2.6%
(Missing) 37
97.4%

Length

2024-10-26T20:10:28.693580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-26T20:10:28.823973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1
33.3%
. 1
33.3%
0 1
33.3%

ZL23
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MJ23
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MNO23
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

ZL24
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MJ24
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MNO24
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

ZL25
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MJ25
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MNO25
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

ZL26
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MJ26
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

MNO26
Unsupported

Missing  Rejected  Unsupported 

Missing38
Missing (%)100.0%
Memory size432.0 B

Interactions

2024-10-26T20:09:40.716933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.066072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.984922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.933931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.865954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.828472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.685821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:40.874121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.204271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.102553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.064067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.018836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.954014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.843526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:41.003567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.327684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.215389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.232834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.154294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.076528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.974023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:41.119889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.453640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.322026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.348127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.264416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.185515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:40.099394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:41.244377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.600433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.545527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.504158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.435926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.334394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:40.238977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:41.364061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.731356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.685897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.614231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.575482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.455904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:40.370251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:41.495326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:35.878673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:36.817734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:37.736640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:38.704340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:39.573552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-26T20:09:40.605818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-26T20:10:28.955185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ICICPKAPACITA (t/rok)LINEKMJ20MJ21MJ5MJ6ODPADROKS42_XS42_YZL10ZL11ZL12ZL13ZL14ZL15ZL16ZL17ZL18ZL19ZL20ZL21ZL5ZL6ZL7ZL8ZL9
IC1.000-0.1640.0990.0000.3860.0000.5190.386-0.1310.149-0.284-0.0210.4540.4540.4540.4540.4540.4540.4540.3860.1910.1910.1910.0000.5190.4540.4540.4540.454
ICP-0.1641.000-0.0110.0000.2780.5350.0000.278-0.002-0.1290.076-0.0640.1860.1860.1860.1860.1860.1860.1860.2780.2410.2410.2410.5350.0000.1860.1860.1860.186
KAPACITA (t/rok)0.099-0.0111.0000.5600.7560.6550.0000.7560.727-0.397-0.0510.1380.4450.4450.4450.4450.4450.4450.4450.7560.8520.8520.8520.6550.0000.4450.4450.4450.445
LINEK0.0000.0000.5601.0000.2670.0000.0000.2670.5600.1950.0000.0000.0000.0000.0000.0000.0000.0000.0000.2670.2180.2180.2180.0000.0000.0000.0000.0000.000
MJ200.3860.2780.7560.2671.0001.0000.0000.9150.6750.3610.0000.1790.9810.9810.9810.9810.9810.9810.9810.9150.9810.9810.9811.0000.0000.9810.9810.9810.981
MJ210.0000.5350.6550.0001.0001.0001.0001.0000.6550.6550.5350.0001.0001.0001.0001.0001.0001.0001.0001.0000.2750.2750.2750.2751.0001.0001.0001.0001.000
MJ50.5190.0000.0000.0000.0001.0001.0000.0000.0000.2260.0000.0000.9810.9810.9810.9810.9810.9810.9810.0000.0000.0000.0001.0000.4500.9810.9810.9810.981
MJ60.3860.2780.7560.2670.9151.0000.0001.0000.6750.3610.0000.1790.9810.9810.9810.9810.9810.9810.9810.9150.9810.9810.9811.0000.0000.9810.9810.9810.981
ODPAD-0.131-0.0020.7270.5600.6750.6550.0000.6751.000-0.392-0.1200.1580.3710.3710.3710.3710.3710.3710.3710.6750.8120.8120.8120.6550.0000.3710.3710.3710.371
ROK0.149-0.129-0.3970.1950.3610.6550.2260.361-0.3921.000-0.0830.1650.2860.2860.2860.2860.2860.2860.2860.3610.4510.4510.4510.6550.2260.2860.2860.2860.286
S42_X-0.2840.076-0.0510.0000.0000.5350.0000.000-0.120-0.0831.000-0.5150.0000.0000.0000.0000.0000.0000.0000.0000.2020.2020.2020.5350.0000.0000.0000.0000.000
S42_Y-0.021-0.0640.1380.0000.1790.0000.0000.1790.1580.165-0.5151.0000.0000.0000.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0000.0000.000
ZL100.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL110.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL120.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL130.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL140.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL150.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL160.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL170.3860.2780.7560.2670.9151.0000.0000.9150.6750.3610.0000.1790.9810.9810.9810.9810.9810.9810.9811.0000.9810.9810.9811.0000.0000.9810.9810.9810.981
ZL180.1910.2410.8520.2180.9810.2750.0000.9810.8120.4510.2020.0000.6780.6780.6780.6780.6780.6780.6780.9811.0001.0001.0000.2750.0000.6780.6780.6780.678
ZL190.1910.2410.8520.2180.9810.2750.0000.9810.8120.4510.2020.0000.6780.6780.6780.6780.6780.6780.6780.9811.0001.0001.0000.2750.0000.6780.6780.6780.678
ZL200.1910.2410.8520.2180.9810.2750.0000.9810.8120.4510.2020.0000.6780.6780.6780.6780.6780.6780.6780.9811.0001.0001.0000.2750.0000.6780.6780.6780.678
ZL210.0000.5350.6550.0001.0000.2751.0001.0000.6550.6550.5350.0001.0001.0001.0001.0001.0001.0001.0001.0000.2750.2750.2751.0001.0001.0001.0001.0001.000
ZL50.5190.0000.0000.0000.0001.0000.4500.0000.0000.2260.0000.0000.9810.9810.9810.9810.9810.9810.9810.0000.0000.0000.0001.0001.0000.9810.9810.9810.981
ZL60.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL70.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL80.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000
ZL90.4540.1860.4450.0000.9811.0000.9810.9810.3710.2860.0000.0001.0001.0001.0001.0001.0001.0001.0000.9810.6780.6780.6781.0000.9811.0001.0001.0001.000

Missing values

2024-10-26T20:09:41.925375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-26T20:09:42.544085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-26T20:09:44.013372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ICS42_XS42_YICPNAZEVPOZNNAZEV_PULICE_POBEC_PNAZEV_ZULICE_ZOBEC_ZROKDALDRUHYKAPACITA (t/rok)ODPADLINEKLINKYPLYNYZL1MJ1MNO1ZL2MJ2MNO2ZL3MJ3MNO3ZL4MJ4MNO4ZL5MJ5MNO5ZL6MJ6MNO6ZL7MJ7MNO7ZL8MJ8MNO8ZL9MJ9MNO9ZL10MJ10MNO10ZL11MJ11MNO11ZL12MJ12MNO12ZL13MJ13MNO13ZL14MJ14MNO14ZL15MJ15MNO15ZL16MJ16MNO16ZL17MJ17MNO17ZL18MJ18MNO18ZL19MJ19MNO19ZL20MJ20MNO20ZL21MJ21MNO21ZL22MJ22MNO22ZL23MJ23MNO23ZL24MJ24MNO24ZL25MJ25MNO25ZL26MJ26MNO26
02725323634769955517233602190081Nemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského kraje – Kotelna a spalovnaNaNNemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského krajeMáchova 40025601 BenešovNemocnice Rudolfa a Stefanie Benešov, a.s., nemocnice Støedoèeského kraje – Kotelna a spalovnaMáchova 40025601 Benešov20012004 – instalace technologie na záchyt PCDD/F, 2009 – rekonstrukce systému na èištìní spalinnemocnièní a zdravotnické odpady10008061spalovna PL-10-200suchá vypírka spalin (adsorpce), tkaninový rukávcový filtr, dioxinový filtr s aktivním koksem, alkalická vypírka spalinTZLt/rok0,059SO2t/rok0,204NOxt/rok3,662COt/rok0,449Ct/rok0,11Sbkg/rok0,003Askg/rok0,013Cdkg/rok0,003Crkg/rok0,043Hgkg/rok0,442Cokg/rok0,003Mnkg/rok0,066Cukg/rok0,021Nikg/rok0,025Pbkg/rok0,016Vkg/rok0,007Tlkg/rok0,002Fkg/rok9,4Clkg/rok58,7PCDD+PCDFmg/rok0,27NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14527464934138645605943604340041ÈEZ, a.s. ? Elektrárna LedviceZmìnou integrovaného povolení è. 27 ze dne 31. 10. 2022 byla povolena spalovací zkouška kalù z likvidace odpadních vod spolu s hnìdým uhlím. Emise pocházejí pøevážnì ze spalování uhlí.ÈEZ, a.s.Duhová 144414000 Praha 4ÈEZ, a.s. ? Elektrárna LedviceOsada 14141801 Bílina2022NaNkaly z likvidace odpadních vod ( 19 08 14 ? kaly z jiných zpùsobù èištìní prùmyslových odpadních vod neuvedené pod èíslem 19 08 13)70151blok B4 s fluidním kotlemsuché odsíøení pomocí vápence ve fluidním loži, elektrostatický odluèovaèTZLt/rok23,258SO2t/rok630,381NOxt/rok522,117COt/rok25,36Sbkg/rok2,624Askg/rok64,29Cdkg/rok3,936Crkg/rok83,315Hgkg/rok20,337Cokg/rok36,767Mnkg/rok1036,515Cukg/rok28,865Nikg/rok43,297Pbkg/rok36,737Vkg/rok27,553Znkg/rok176,47Tlkg/rok24,929Fkg/rok276,841Clkg/rok1280,063PCDD+PCDFmg/rok50,251NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
26071347036215055452525611110451SAKO Brno, a.s. – divize 3 ZEVONaNSAKO Brno, a.s.Jedovnická 424762800 BrnoSAKO Brno, a.s. – divize 3 ZEVOJedovnická 424762800 Brno19891994 – spuštìní 2. stupnì èištìní spalin, 2004 – SNCR, 2009–2010 – rekonstrukce zaøízenísmìsný komunální odpad2480002425322spalovenské kotle K2 a K3 (systém Düsseldorf)nekatalytická redukce oxidù dusíku nástøikem roztoku moèoviny, aktivní uhlí, absorpce plynù vápennou vypírkou, textilní filtr s vláknitou vrstvouTZLt/rok0,326SO2t/rok39,962NOxt/rok307,607COt/rok8,6Ct/rok1,239NH3t/rok1,547Sbkg/rok0,653Askg/rok0,331Cdkg/rok0,057Crkg/rok1,785Hgkg/rok0,41Cokg/rok0,021Mnkg/rok0,651Cukg/rok6,616Nikg/rok1,246Pbkg/rok4,249Vkg/rok0,048Znkg/rok30,91Tlkg/rok0,018Fkg/rok130Clkg/rok10804PCDD+PCDFmg/rok6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32877141935498075464870612958121Envir s.r.o. – Spalovna NO BrtniceNa základì písemného oznámení provozovatele je od 2. 3. 2015 pozastaveno spalování odpadu z dùvodu odpojení spalovny od dodávek elektrické energie, do konce roku 2022 nebylo zahájeno. Bylo zahájeno posuzování vlivù na životní prostøedí k zámìru „Modernizace spalovny NO Brtnice s cílem navýšení zpracovatelské kapacity“. Plánovaná kapacita je 2 800 t/rok.Envir s.r.o.Buštìhradská 99827201 KladnoEnvir s.r.o. – Spalovna NO BrtnicePod Kaplí 17958832 Brtnice19962006 – renovace filtru GORE-TEX, 2011 – instalace kontinuálního mìøení emisíNaN40001dvoustupòová spalovací pec HOVAL GG 17 (pyrolýzní komora a dopalovací termoreaktor)suchý filtr GORE-TEX, praèka kouøových plynù DRY-WET ÖSKONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41486749434317315595892624100031Lafarge Cement, a.s.Spoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèní peci, pouze malá èást ze spoluspalování odpadu.Lafarge Cement, a.s.Èížkovice 2741112 ÈížkoviceLafarge Cement, a.s.Èížkovice 2741112 Èížkovice19752006 – SNCRhnìdouhelný generátorový dehet, stabilizované kaly (sludge), spalitelný odpad (palivo vyrobené z odpadu), celé i drcené pneumatiky, surový odpadní benzin, odpadní oleje, øedidla a glycerin, Lipix, døevní odpad, drcená odpadní pryž, vlastní odpady, briketovaný odpad (skelná vlákna/plast)1300001121501rotaèní pec na výrobu slínkuelektrostatický tøíkomorový odluèovaèTZLt/rok3,476SO2t/rok548,823NOxt/rok659,592COt/rok1550,205Ct/rok20,368NH3t/rok3,074Sbkg/rok1,88Askg/rok0,98Cdkg/rok0,11Crkg/rok1,81Hgkg/rok1,56Cokg/rok0,41Mnkg/rok1,64Cukg/rok0,78Nikg/rok0,62Pbkg/rok1,39Vkg/rok0,31Tlkg/rok1,93Fkg/rok66Clkg/rok553PCDD+PCDFmg/rok10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52563895536900955456356635988081Recovera Využití zdrojù a.s. – Spalovna NO ZlínOd 19 .4. 2022 došlo ke zmìnì názvu provozovatele (døíve SUEZ CZ a.s.).Recovera Využití zdrojù a.s.Španìlská 107312000 Praha 2Recovera Využití zdrojù a.s. – Spalovna NO ZlínTøída 3.kvìtna 118076302 Zlín1993NaNodpady z rùzných odvìtví prùm. èinnosti, odpady ze zdravotní a veterinární péèe473043772spalovací linky SP 3202/Epolosuchá vypírka spalin, textilní filtry s vláknitou vrstvou s automatickým oklepemTZLt/rok0,005SO2t/rok0,076NOxt/rok2,178COt/rok0,252Ct/rok0,024Sbkg/rok0,234Askg/rok0,123Cdkg/rok0,025Crkg/rok0,037Hgkg/rok0,025Cokg/rok0,05Mnkg/rok4,51Cukg/rok0,197Nikg/rok0,049Pbkg/rok0,16Vkg/rok0,025Tlkg/rok0,234Fkg/rok2,47Clkg/rok61,18PCDD+PCDFmg/rok0,097NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
617990635591885563631646870171Fakultní nemocnice Hradec Králové – Spalovna NO, kotelna + kotel CERTUSNaNFakultní nemocnice Hradec KrálovéSokolská 58150005 Hradec KrálovéFakultní nemocnice Hradec Králové – Spalovna NO, kotelna + kotel CERTUSSokolská 58150005 Hradec Králové2017NaNnemocnièní a zdravotnické odpady190010561spalovna nemocnièních odpadù SU-24 Pabsorpèní praèka plynù, tkaninový filtr, dioxinový filtrTZLt/rok0SO2t/rok0,045NOxt/rok1,952COt/rok0,045Ct/rok0,034Sbkg/rok0,06Askg/rok0,09Cdkg/rok0,03Crkg/rok0,41Hgkg/rok0,049Cokg/rok0,32Mnkg/rok0,64Cukg/rok2,06Nikg/rok0,26Pbkg/rok0,37Vkg/rok0,13Tlkg/rok0,06Fkg/rok1Clkg/rok59PCDD+PCDFmg/rok0,134NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71550407736998935495563647680111Cement Hranice, akciová spoleènostSpoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèní peci, pouze malá èást ze spoluspalování odpadu.Cement Hranice, akciová spoleènostBìlotínská 28875301 HraniceCement Hranice, akciová spoleènostBìlotínská 28875301 Hranice19922017 – náhrada dvou elektroodluèovaèù za rotaèní pecí jedním textilním hadicovým filtremodpady ze zpracování døeva, plastový odpad, absorpèní èinidla, filtraèní materiály, èisticí tkaniny a ochranné odìvy, pneumatiky, plasty a kauèuk, textil, spalitelný odpad (palivo vyrobené z odpadu), odpady z mechanické úpravy odpadu, kaly z èištìní komunálních odpadních vod80000606691rotaèní pec na výrobu slínkutextilní hadicový filtr, SNCRTZLt/rok4,014SO2t/rok33,116NOxt/rok644,773COt/rok2944,708Ct/rok32,603NH3t/rok28,258Sbkg/rok2,487Askg/rok1,287Cdkg/rok0,176Crkg/rok0,999Hgkg/rok19,539Cokg/rok0,508Mnkg/rok1,643Cukg/rok1,043Nikg/rok0,626Pbkg/rok1,287Vkg/rok0,365Tlkg/rok2,628Fkg/rok36Clkg/rok3414PCDD+PCDFmg/rok13,186NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
86072777235447255476191648696033SLUŽBY MÌSTA JIHLAVY s.r.o. – ÈOV JihlavaDne 3. 5. 2013 bylo zaøízení spalovny z provozních dùvodù odstaveno, nebylo uvedeno do provozu ani do konce roku 2022.SLUŽBY MÌSTA JIHLAVY s.r.o.Havlíèkova 21858601 JihlavaSLUŽBY MÌSTA JIHLAVY s.r.o. – ÈOV JihlavaHruškové Dvory 6958601 Jihlava2007NaNNaN740101linka termické degradace kalù PS 80technologie NEUTREC s následným tøístupòovým systémem èištìní spalin typu PCDD/F s využitím adsorpèních úèinkù aktivního uhlíNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
94979048033789915519315653270043Plzeòská teplárenská, a.s. – ZEVO PlzeòZmìnou integrovaného povolení è. 7 ze dne 14. 11. 2022 byla navýšena kapacita zaøízení z pùvodních 110 000 t/rok.Plzeòská teplárenská, a.s.Doubravecká 276030100 PlzeòPlzeòská teplárenská, a.s. – ZEVO Plzeòlokalita skládky TKO Chotíkov 49233017 Chotíkov2016NaNsmìsný komunální odpad1200001116981parní roštový kotelrozprašovací sušárna, tkaninový filtr s dávkováním sorbentu (smìs hydroxidu vápenatého a aktivního uhlí), dvoustupòová praèka spalin, SCR pomocí èpavkuTZLt/rok0,065SO2t/rok3,18NOxt/rok47,142COt/rok6,417Ct/rok0,713Sbkg/rok3,25Askg/rok3,25Cdkg/rok3,25Crkg/rok3,25Hgkg/rok3,53Cokg/rok3,25Mnkg/rok3,25Cukg/rok16,95Nikg/rok37,76Pbkg/rok3,25Vkg/rok3,25Tlkg/rok3,25Fkg/rok79,961Clkg/rok47,013PCDD+PCDFmg/rok5,8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
ICS42_XS42_YICPNAZEVPOZNNAZEV_PULICE_POBEC_PNAZEV_ZULICE_ZOBEC_ZROKDALDRUHYKAPACITA (t/rok)ODPADLINEKLINKYPLYNYZL1MJ1MNO1ZL2MJ2MNO2ZL3MJ3MNO3ZL4MJ4MNO4ZL5MJ5MNO5ZL6MJ6MNO6ZL7MJ7MNO7ZL8MJ8MNO8ZL9MJ9MNO9ZL10MJ10MNO10ZL11MJ11MNO11ZL12MJ12MNO12ZL13MJ13MNO13ZL14MJ14MNO14ZL15MJ15MNO15ZL16MJ16MNO16ZL17MJ17MNO17ZL18MJ18MNO18ZL19MJ19MNO19ZL20MJ20MNO20ZL21MJ21MNO21ZL22MJ22MNO22ZL23MJ23MNO23ZL24MJ24MNO24ZL25MJ25MNO25ZL26MJ26MNO26
282620957834525345540821738620091Èeskomoravský cement, a.s. – Závod RadotínSpoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèních pecích, pouze malá èást ze spoluspalování odpadu. Prùbìžné využívání odpadù probíhá v závislosti na potøebì paliva v cementárnì od dubna 2015 obvykle jeden den v týdnu.Èeskomoravský cement, a.s.Mokrá 35966404 Mokrá-HorákovÈeskomoravský cement, a.s. – Závod RadotínK cementárnì 126115302 Praha 1619612018 – výmìna hoøáku rotaèní pece è. 2spalitelný odpad (palivo vyrobené z odpadu)88000143752rotaèní pece na výrobu slínkuelektrostatické odluèovaèeTZLt/rok0,644SO2t/rok53,22NOxt/rok713,975COt/rok186,037Ct/rok7,679NH3t/rok34,055Sbkg/rok5,569Askg/rok3,27Cdkg/rok10,451Crkg/rok8,254Hgkg/rok7,75Cokg/rok1,316Mnkg/rok66,601Cukg/rok14,06Nikg/rok6,019Pbkg/rok5,256Vkg/rok0,754Tlkg/rok6,472Fkg/rok6Clkg/rok99PCDD+PCDFmg/rok21,6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
294935608935499235546405743850321AVE CZ odpadové hospodáøství s.r.o. – Provozovna PardubiceSpalovna je mimo provoz od 30. 1. 2004, do konce roku 2022 nebyla zprovoznìna. Dne 26. 6. 2012 byla podána žádost o integrované povolení. Dne 3. 6. 2021 nabylo právní moci rozhodnutí o zastavení øízení, protože v urèené lhùtì nebyly doplnìny potøebné podklady. Provozovatel mùže v budoucnu o vydání integrovaného povolení opìtovnì zažádat.AVE CZ odpadové hospodáøství s.r.o.Pražská 132110200 Praha 10AVE CZ odpadové hospodáøství s.r.o. – Provozovna Pardubice53354 Rybitví19941997 – rekonstrukce pøehøíváku páryNaN1580001spalovna BÈOVdva textilní filtry, absorpce plynù s chemickou reakcíNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
302620957836292435455260747840061Èeskomoravský cement, a.s. – Závod MokráSpoluspalování – emise pocházejí pøevážnì z výroby slínku v rotaèních pecích, pouze malá èást ze spoluspalování odpadu.Èeskomoravský cement, a.s.Mokrá 35966404 Mokrá-HorákovÈeskomoravský cement, a.s. – Závod MokráMokrá 35966404 Mokrá-Horákov19682004 – denitrifikace spalin, 2018 – modernizace expedice volnì loženého cementu, 2022 – instalace nového kontinuálního analyzátoru, dávkování cementových komponent ze silapneumatiky, odpadní surový benzin, zbytkový produkt oxoalkoholù, plastový odpad, èistírenské odpadní kaly, spalitelný odpad (palivo vyrobené z odpadu), odpadní rozpouštìdla113800800902rotaèní pece RPS 1 a RPS 2 na výrobu slínkuelektrostatické odluèovaèeTZLt/rok1,637SO2t/rok4,398NOxt/rok396,06COt/rok2693,386Ct/rok26,628NH3t/rok22,588Sbkg/rok2,967Askg/rok1,543Cdkg/rok0,168Crkg/rok6,949Hgkg/rok20,336Cokg/rok0,897Mnkg/rok3,26Cukg/rok1,805Nikg/rok1,901Pbkg/rok1,901Vkg/rok0,399Tlkg/rok3,006Fkg/rok107,9Clkg/rok1096,6PCDD+PCDFmg/rok9,1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
316145936434208025459097755910031RUMPOLD s.r.o. – Spalovna StrakoniceDne 5. 12. 2022 byla vydána zmìna rozhodnutí, kterou byla navýšena kapacita z pùvodních 15 000 t/rok.RUMPOLD s.r.o.Klimentská 174611000 Praha 1RUMPOLD s.r.o. – Spalovna StrakoniceHeydukova 111138601 Strakonice19902004 – suché sorpèní dvoustupòové èištìní NEUTRECzdravotnické odpady, odpadní oleje, sorbenty, obaly, odpady z rùzných odvìtví prùm. èinnosti180016311dvoustupòová spalovací pec HOVAL GG 24 (pyrolýzní komora a dopalovací termoreaktor)suché sorpèní dvoustupòové èištìní NEUTREC (pøedèištìní pomocí NaHCO3, dávkování smìsi jemnì mletého Ca(OH)2 a aktivního uhlí Norit GL 50, textilní rukávcový filtrTZLt/rok0,115SO2t/rok0,132NOxt/rok0,016COt/rok0,018Ct/rok0,023Sbkg/rok0,108Askg/rok0,055Cdkg/rok0,007Crkg/rok0,069Hgkg/rok0Cokg/rok0,129Mnkg/rok1,347Cukg/rok0,113Nikg/rok2,423Pbkg/rok0,343Vkg/rok0,012Tlkg/rok0,108Fkg/rok3,262Clkg/rok17,941PCDD+PCDFmg/rok0,04NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
324219492036175445549363765490013Marius Pedersen a.s. ? Vojenský zdravotní ústav Tìchonín, Odbor biologické ochrany ? Spalovna nebezpeèných odpadùSpalovna je v majetku spoleènosti Armádní Servisní, pøíspìvková organizace, provozovatelem je Marius Pedersen a.s. Od 10. 12. 2013 byla odstavena z provozu do doby, než bude instalováno zaøízení k omezení emisí NOx. Dne 19. 11. 2021 bylo vydáno povolení provozu a schválen provozní øád po provedené rekonstrukci. Spalovna byla od vydání povolení provozu poprvé spuštìna na mìøení emisí dne 22. 2. 2022.Marius Pedersen a.s.Prùbìžná 194050009 Hradec KrálovéMarius Pedersen a.s. ? Vojenský zdravotní ústav Tìchonín, Odbor biologické ochrany ? Spalovna nebezpeèných odpadùAreál Centra biologické ochrany armády Èeské republiky56166 Tìchonín20072017–2021 – modernizace technologie, denitrifikaceinfekèní odpady, absorpèní èinidla, filtraèní materiály, èisticí tkaniny a ochranné odìvy zneèištìné nebezpeènými látkami9011spalovna SMS Rokycany se spalovací komorou SLK 100/4dávkování smìsi NaHCO3 a aktivního uhlí Norit GL 560, SNCR, textilní rukávcový filtrTZLt/rok0SO2t/rok0NOxt/rok0,001COt/rok0Ct/rok0Sbkg/rok0Askg/rok0Cdkg/rok0Crkg/rok0Hgkg/rok0Cokg/rok0Mnkg/rok0Cukg/rok0Nikg/rok0Pbkg/rok0Vkg/rok0Tlkg/rok0Fkg/rok1Clkg/rok0,109PCDD+PCDFmg/rok0,253NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
332766091536793325439632772840161Uherskohradišská nemocnice a.s. – Kotelna nemocnice a spalovna NONaNUherskohradišská nemocnice a. s.J. E. Purkynì 36568668 Uherské HradištìUherskohradišská nemocnice a.s. – Kotelna nemocnice a spalovna NOJ. E. Purkynì 36568668 Uherské Hradištì19962007 – modernizace spalovny, rekonstrukce èisticího zaøízení, dioxinový filtr (systém REMEDIA)nemocnièní a zdravotnické odpady3503491dvoustupòová spalovací pec HOVAL GG 7 (pyrolýzní komora a dopalovací termoreaktor), typ VO 180suchá vápenná sorpce + Sorbalit, hadicový dioxinový filtr (systém REMEDIA), dvoustupòová absorpèní vypírka pomocí NaOHTZLt/rok0,004SO2t/rok0,02NOxt/rok0,767COt/rok0,01Ct/rok0,001Sbkg/rok0Askg/rok0Cdkg/rok0Crkg/rok0,018Hgkg/rok0,15Cokg/rok0Mnkg/rok0Cukg/rok0,018Nikg/rok0,017Pbkg/rok0Vkg/rok0Tlkg/rok0Fkg/rok0,5Clkg/rok1PCDD+PCDFmg/rok0,13NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
342820788234312715614923774878221CHS Epi, a.s.Spaluje pouze vlastní odpady.CHS Epi, a.s.Revoluèní 193040032 Ústí nad LabemCHS Epi, a.s.Revoluèní 193040032 Ústí nad Labem20022003 – dioxinový filtrvlastní odpady provozu EPITETRA, pøevážnì odpadní chlorované uhlovodíky a odplyny500026301spalovna odpadních chlorovaných uhlovodíkù PS-13alkalická vypírka pomocí NaOH s pøídavkem peroxidu vodíku, dioxinový filtr s aktivním uhlímTZLt/rok0,028SO2t/rok0,066NOxt/rok2,804COt/rok0,018Ct/rok0,01Sbkg/rok0,024Askg/rok0,104Cdkg/rok0,457Crkg/rok1,429Hgkg/rok0,082Cokg/rok0,2Mnkg/rok1,034Cukg/rok1,216Nikg/rok0,944Pbkg/rok0,523Vkg/rok0,111Tlkg/rok0,04Fkg/rok3,179Clkg/rok11,828PCDD+PCDFmg/rok0,2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
352563895534289825612951774970301Recovera Využití zdrojù a.s. – Spalovna prùmyslových odpadù TrmiceOd 19 .4. 2022 došlo ke zmìnì názvu provozovatele (døíve SUEZ CZ a.s.).Recovera Využití zdrojù a.s.Španìlská 107312000 Praha 2Recovera Využití zdrojù a.s.– Spalovna prùmyslových odpadù TrmiceNa Rovném 86540004 Trmice19932004 – zaøízení pro záchyt PCDD/Fodpady z rùzných odvìtví prùm. èinnosti, odpady ze zdravotní a veterinární péèe1600097253samostatné spalovací linky – rotaèní pece RC 198/158 300 EGdioxinový a prachový filtr, tøístupòová mokrá vypírka (odprášení, vodní a alkalická vypírka)TZLt/rok0,292SO2t/rok1,359NOxt/rok10,745COt/rok1,962Ct/rok0,099Sbkg/rok1,41Askg/rok0,537Cdkg/rok0,2Crkg/rok8,391Hgkg/rok3,213Cokg/rok0,141Mnkg/rok1,061Cukg/rok13,931Nikg/rok1,913Pbkg/rok10,002Vkg/rok0,131Tlkg/rok0,089Fkg/rok19,014Clkg/rok280,124PCDD+PCDFmg/rok5,7NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
361183537151525486845776430491DEZA, a.s. – SpalovnaNaNDEZA, a.s.Masarykova 75375701 Valašské MeziøíèíDEZA, a.s. – SpalovnaMasarykova 75375701 Valašské Meziøíèí2000NaNodpady z èistíren odp. vod, odpady z rùzných odvìtví prùm. èinnosti, obaly, tkaniny, zdrav. odpady, nátìr. hmoty1000068741spalovna prùmyslových odpadù – rotaèní pec s dohoøívací komoroutkaninový tøíkomorový filtr, mokrá tøístupòová alkalická vypírkaTZLt/rok0,062SO2t/rok0,107NOxt/rok13,539COt/rok0,007Ct/rok0,053Sbkg/rok0,672Askg/rok0,672Cdkg/rok0,672Crkg/rok0,672Hgkg/rok2,626Cokg/rok0,672Mnkg/rok0,672Cukg/rok0,672Nikg/rok0,672Pbkg/rok1,679Vkg/rok0,672Tlkg/rok0,672Fkg/rok20,147Clkg/rok11,653PCDD+PCDFmg/rok0,244NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
379258435772515415800793410111Nemocnice Znojmo, pøíspìvková organizace – Kotelna a spalovnaNaNNemocnice Znojmo, pøíspìvková organizaceMUDr. Jana Janského 267566902 ZnojmoNemocnice Znojmo, pøíspìvková organizace – Kotelna a spalovnaMUDr. Jana Janského 267566902 Znojmo19942004 – instalace nové suché technologie èištìní spalinnemocnièní a zdravotnické odpady7808471dvoustupòová spalovací pec HOVAL GG 14 (pyrolýzní komora a dopalovací termoreaktor)suchý filtr s dávkováním aktivních látek pro záchyt tìžkých kovù a dioxinùTZLt/rok0,002SO2t/rok0,021NOxt/rok1,076COt/rok0,125Ct/rok0,015Sbkg/rok0,011Askg/rok0,011Cdkg/rok0,001Crkg/rok0,316Hgkg/rok0,015Cokg/rok0,003Mnkg/rok0,239Cukg/rok0,087Nikg/rok1,252Pbkg/rok0,015Vkg/rok0,011Tlkg/rok0,011Fkg/rok0,207Clkg/rok9,1PCDD+PCDFmg/rok0,02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN